Fault detection and diagnosis for large solar thermal systems: A review of fault types and applicable methods

Solar Energy 197 (2020) 472–484

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Fault detection and diagnosis for large solar thermal systems: A review of fault types and applicable methods

T

⁎

Gaëlle Faure , Mathieu Vallée, Cédric Paulus, Tuan Quoc Tran Univ. Grenoble Alpes, INES, F-73375 Le Bourget du Lac, France CEA, LITEN, 17 rue des Martyrs, F-38054 Grenoble, France

A R T I C LE I N FO

A B S T R A C T

Keywords: Solar thermal Fault detection Fault diagnosis Failure modes, effects and criticality analysis

All technical processes are subject to dysfunctions during their lifespan, and large solar thermal systems (LSTS) are no exception to this rule. The development of robust fault detection and diagnosis (FDD) methods is therefore a key issue. This paper reports on a review of faults types that can affect LSTS as well as the current approaches to detect and diagnose them. After a brief description of the system, a literature review of its dysfunctions is presented and the results of a study to complete and organize the inventory of these faults are described. The critical faults are defined. The state of the art of the research concerning FDD methods for LSTS is then detailed and demonstrates that this topic is of current interest. Finally, the performance of current algorithms is evaluated by two different ways: first along a list of desirable characteristics of a FDD method for LSTS, second along the ability of each method to detect the critical faults. This evaluation shows that there is room for some improvements in detecting and diagnosing faults for LSTS and these avenues are discussed.

1. Introduction Large solar thermal systems (LSTS) can provide renewable and low cost energy to district heating networks and industrial processes (Mekhilef et al., 2011; Taibi et al., 2012). Over the last 25 years, many of them have been built, mostly in Northern European countries. 2016 was a record year with almost 500 000 m2 of newly installed solar collectors for district heat production (Weiss et al., 2017). Around 30% of this new capacity is due to the commissioning of the world’s biggest installation in Silkeborg (Denmark) with a total thermal power of 110 MW. As to industrial processes, a study listed more than 500 systems in the world in operation in early 2017 with a total installed capacity of solar collectors of 416 414 m2 (Sun and Wind Energy, 2017). LSTS received a significant interest for several years and studies show that their potential of development is large (Mekhilef et al., 2011; Sharma et al., 2017; Taibi et al., 2012). As any complex and technical systems, LSTS can be subject to faults. In the following, a fault refers to the “unpermitted deviation of, at least, one characteristic property or parameter of the system from the acceptable/usual/standard condition”, as defined by the International Federation of Automatic Control (IFAC, n.d.). This deviation can be permanent or intermittent, abrupt or progressive. In any case, faults can entail a degradation of the production yield and/or additional maintenance costs. A failure is the result of one or several faults and is the ⁎

condition of not being able to meet an intended objective i.e. producing thermal energy with a good efficiency. In LSTS, like in other renewable energy systems, not detecting and accurately identifying faults can significantly hinder the return on investment and the competitiveness of solar thermal energy. Fortunately, large systems enable more monitoring and more opportunities for applying advanced fault detection and diagnosis (FDD) methods, compared to more widespread solar domestic hot water systems. While FDD methods for solar domestic hot water have been developed for several decades (Barker et al., 1990; Duff and Millard, 1980; Murthy et al., 1989), the research about large thermal solar installations remains marginal. Development of this technology in the near future requires designing robust FDD methods. Thus, this paper intends to give a state of the art concerning the issue of detecting and diagnosing faults in LSTS with the aim of identifying the potential improvements of the approaches. Moreover, to the best of our knowledge, there is no study combining an overview of faults which can affect LSTS and a review of available methods to detect and diagnose them. The accurate identification of a fault is however critical in the choice of the suitable method to detect it. In this paper, we review faults and diagnosis techniques for LSTS and evaluate the performance of these techniques. In a first part, we describe the studied system as long as the faults which can affect it. The critical faults are derived from a Failure Modes, Effects and Criticality

Corresponding author. E-mail address: [email protected] (G. Faure).

https://doi.org/10.1016/j.solener.2020.01.027 Received 1 February 2019; Received in revised form 7 January 2020; Accepted 11 January 2020 0038-092X/ © 2020 International Solar Energy Society. Published by Elsevier Ltd. All rights reserved.

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• External heat exchanger(s): optional components to decouple the

Analysis (FMECA). In a second part, we present the current FDD methods for LSTS sorted by the type of knowledge used by each method. In the third section, we evaluate the performance of the FDD methods using two approaches. In the first approach, each FFD method is tested against six previously identified desirable characteristics selected from (Venkatasubramanian et al., 2003). The second method ranks the FDD methods by their ability to detect and diagnose the most critical faults identified in the first section.

• •

solar collection system from the main system, which is usually filled with a different fluid. Secondary transport: hydraulic components between heat exchanger or storage and the main system. Controller: control-command components and sensors.

Each of these sub-systems can be subjected to dysfunctions, with various characteristics and various degrees of criticality.

2. A review of faults in large solar thermal systems 2.2. Typical monitoring of a large solar thermal system In this section, we conduct a review and classification of possible faults in a LSTS. This review is based both on a literature study and on a dedicated study we presented in a previous paper (Faure et al., 2016). Sections 2.1 and 2.2 first describe the studied system. Then Section 2.3 presents the result of our literature review about dysfunctions that can affect LSTS. Finally, Section 2.4 describes the methodology to identify the major dysfunctions of a LSTS.

In order to detect and diagnose the possible dysfunctions, the system is monitored. The characteristics of the final FDD procedure are strongly dependent on the measured values. Thereafter we will assume the following typical set of sensors:

• One pyranometer in the plane of the solar collectors (at the same tilt), • Two flow meters: one on the primary transport and one on the secondary transport, • One absolute pressure sensor on the primary transport, • Thirteen temperature sensors located in various positions along the

2.1. Definition of a large solar thermal system (LSTS) A solar thermal system aims at collecting energy from solar irradiation to heat a fluid, which can further be used for industrial processes, domestic hot water production or district heating. In the present work, the focus is on large solar thermal systems (LSTS) with a total collector area above 500 m2, for the production of hot water at low and medium temperature (80–120 °C). This category of system is usually pressurized, filled with a water-glycol mixture, and may contain a thermal energy storage. However, we do not consider auxiliary heating systems which might take over when solar production is not sufficient. Additionally, we do not specifically consider concentrated solar power (CSP) systems in this review, although some of our conclusions would also apply to this type of system. Fig. 1 presents a typical hydraulic scheme of a LSTS (Peuser et al., 2005; Sorensen, 2012). The whole system can be split into six main subsystems:

system.

Their typical location is specified in Fig. 2. The set of sensors shown in Fig. 2 is representative of a typical LSTS control and monitoring system, with the addition of an absolute pressure sensor (Danish District Heating Association, n.d.; Nielsen and Trier, 2014; Trier et al., 2017). This new sensor is proposed since it is cheap, easy to install and allows the direct detection of faults like a leak in the primary loop or the failure of the expansion vessel. The addition of other sensors could be studied, for instance to deal with specific collector designs, but this question is out of the scope of this paper. 2.3. Initial literature review on LSTS reliability

• Solar collection: composed of solar collectors, connections between collectors and fastening system. • Primary transport: hydraulic components from the solar field to the first heat exchanger or storage. Storage: optional storage tank(s) and its (their) internal heat ex•

Keeping in mind the characteristics of the system we have described in the previous subsections, we completed a first literature review of the reliability of LSTS, with a specific focus on studies that provided data on the type and frequency of dysfunctions for each sub-system. We can first notice that the most complete research studies on reliability of solar thermal systems date back from several decades ago

changer(s).

Fig. 1. Decomposition of a LSTS plant into six sub-systems: solar collection (1), primary transport (2), storage (3), external heat exchanger(s) (4), secondary transport (5) and controller (6). Circles with a triangle denote circulating pumps. 473

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Fig. 2. Typical monitoring of a LSTS.

(Chopra, 1980; Chopra and Wolosewicz, 1980; G. Jorgensen, 1984). Some recent examples are the Solarthermie2000 and Solarthermie2000plus studies (Peuser et al., 2005), which are also based on solar systems in operation since the early 80′s. Although these studies provide interesting inputs in terms of methodology, their results are difficult to exploit nowadays, since many of the considered technologies have been improved or are not in use anymore. More recent studies are rather focused on small scale systems and solar domestic hot water (SDHW) systems. They have especially been conducted in relation with governmental programs fostering the installation of solar thermal systems, and sometimes provide data based on monitoring results (ADEME, 2008; Cholin, 2011). Although some findings of these studies can be applied to LSTS, there are many differences in terms of size, kinds of sub-systems and overall installation and maintenance policies, which strongly affect the type and occurrence of potential dysfunctions. As an example, problems with the solar panels’ fastening system are often reported in SDHW, but will likely be not as significant in LSTS due to the standardization of the components. A main drawback of available studies is also the lack of feedback about the occurrence rate of dysfunctions. Although one earlier study provides occurrence data for some faults (G. J. Jorgensen, 1984), a similar study conducted 25 years later concluded in the lack of precision and reliability of available data (Menicucci, 2009). One important issue is the lack of consistency between databases, which often yields biases and contradictory results depending on the information source. Despite the lack of recent and reliable data on dysfunctions in LSTS, we can highlight several important conclusions from this literature review:

• •

2.4. Complementary survey on LSTS failure modes and their classification To address the limitations identified in this initial literature review, the authors conducted and presented a complementary study in Faure et al. (2016). The main goal was to better assess the type, occurrence rate and criticality of failure modes in LSTS, i.e. the different ways a LSTS cannot be able to produce enough energy when required. The applied methodology combines a top-down approach based on a Failure Modes, Effects and Criticality Analysis (FMECA) (Colli, 2015; Sinha and Steel, 2015; Villemeur, 1988) with a bottom-up approach based on a survey for domain experts. In this paper, we focus on the key results of this study, while the interested reader can refer to (Faure et al., 2016) for more detailed information about the methodology and specific results. The FMECA resulted in the identification of 130 possible failure modes. It was thus important to be able to classify them by “priority” in order to select the more significant ones. The following subsection defines the metric used to order the failure modes. Then the most critical failure modes, according to the defined metric, are presented and discussed.

• Primary transport consistently appears to be the most impacted

• •

solar plant and dysfunctions affecting it have a significant impact of the whole system. Secondary transport and heat exchanger appear to have much less dysfunctions, primarily because these are well-known, classical systems. Storage also appears to have few dysfunctions, for the same reasons. However, this could differ in large-scale systems with unusual storage sizing and technologies.

sub-system. In particular, insulation is often lacking or not adequate, especially to resist UV rays and bird attacks. Leaks are a usual source of dysfunction, as well as pressure loss and air bubbles, which can also result from leaks. Finally, especially in large-scale installations, a bad hydraulic balancing between the solar subfields is sometimes reported. Regulation and controllers can have a high number of dysfunctions, often related to poor installation and parameter tuning, as well as wrong placement of sensors. In particular temperature sensors yielding wrong measurement strongly impact the performance of the system. Solar collection may have some dysfunctions, but is less frequently cited. Some of the problems appearing in earlier studies have been fixed in more recent products, however it remains the key part of the

2.4.1. Definition of the key ranking numbers A critical failure mode is defined in this paper as a failure mode which is both frequent and strongly impactful for the whole system. The Failure Risk Priority Number gives an indication of the criticality of a given failure mode for the system. It is computed for each failurei using (Eq. (1)):

FRPNi = ORi × SRi

(1)

where ORi (Occurrence Rating) is a number representing the occurrence rate of failure i , SRi (Severity Rating) is a number describing the effect of failure i on the system. ORi and SRi are discrete ranking values with a scale from 1 to 5. FRPNi is itself a discrete ranking number with a scale from 1 to 25, 1 standing for the less critical failure modes. To compute ORi we first derive a raw “occurrence number” value Oi 474

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LSTS which can be seen as the minimal set of faults which have to be detected by a good FDD method. Concerning the diagnosis part, isolation and localization of dysfunctions on both primary transport and solar collection sub-systems are complex but critical due to the size of the solar field of a LSTS. We now study the current available methods to achieve these goals. The first subsection will briefly recall the terminology of FDD domain and propose a classification of the different FDD methods. Then, the current applications for LSTS will be presented.

Table 1 Criteria to estimate SRi, the rank of a failure according to its effects on the system. SRi

Effect on the system

1 2 3 4

No effect - user does not notice anything Slight and stable drop in yield Progressing drop in yield Significant drop in yield with immediate risk of substantial degradation of the system No more solar production

5

3.1. Terminology and general overview of a fault detection and diagnosis (FDD) method

from the results of the survey and the state of the art using (Eq. (2)):

Oi = 0.45 ∗ Ni + 0.45 ∗

3 ∗ Nhighi + 2 ∗ Nmedi + Nlowi 6

A fault detection and diagnosis method can be described by a series of transformations on process measurements (Venkatasubramanian et al., 2003). The way to split the global procedure and the naming conventions of the different transformations differ slightly from one author to another. Fig. 3 presents a simplified scheme and vocabulary used in this paper. The first step deals with the analysis and combination of measurements in order to extract representative features about the process behavior: this is the feature generation. Then the detection and diagnosis algorithm decides whether the features values are in a normal range. If not, a fault is triggered and the diagnosis part (step 2.2) of the system is launched: an isolation algorithm is used to obtain information about the kind and location of the dysfunction, a fault identification algorithm adds an estimation of its size and time-variant behavior (e.g. abrupt, incipient, or intermittent). At each step of this process, a priori process knowledge is required in order to choose the suitable methods and to properly implement them. Fig. 3 is a simplified representation of a sequential process. In practice, some steps can be executed in parallel and others can be skipped. In particular, depending on the chosen method and the expected outputs, the process can stop just after the detection or the isolation. Moreover, isolation algorithms have varying degrees of accuracy. For instance, the less accurate algorithms provide an approximate class of the fault, whereas the more accurate ones can also isolate the location of the fault. There exists many different FDD methods and several authors proposed a classification (Srinivas Katipamula and Michael R. Brambley, 2005; Venkatasubramanian et al., 2003). Fig. 4 presents the taxonomy based on these previous works and used in this paper to classify the current FDD methods for LSTS. Classes which have currently no applications for LSTS are not included in the Fig. 4. The three upper categories in this taxonomy are distinguished by the origin of the process knowledge involved. On the one hand, both quantitative and qualitative model-based approaches require constructing a model of the supervised system or at least of some parts of the system using a priori expert knowledge. In the quantitative modelbased approach, the model is based on mathematical functional relationships, while in the qualitative model-based approach the model is expressed in terms of qualitative functions centered on different units in a process. On the other hand, in process-history based approaches, knowledge about the process is derived from a large amount of available historical process data. In the following, we review the available FDD methods for LSTS in each of these broad categories.

+ 0.1 ∗ bi (2)

Ni is the total number of citations of a failurei in the survey. Nhighi , Nmedi and Nlowi are respectively the number of “high”, “medium” or “low” frequency qualifications for this failure in the survey. bi is a value ranked between 0 and 2 describing whether the failure is often reported in the literature. We obtain ORi by scaling Oi to an integer between 1 and 5 (the higher the number, the more probable the failure occurrence). Note that a sensitivity analysis showed that the coefficients used in (Eq. (2)) have a low impact on the results of the study. More specifically, using different sets of coefficients only marginally affects the ranking of failure modes along their FRPNs. To compute SRi , we estimated the effect of each failure based on its description and experts’ comments. Possible effect numbers are given in Table 1, and range from 1 (“No effect”) to 5 (“No more solar production”). The resulting FRPNs allow classifying the reported failure modes from the less critical ones, which have a low occurrence rate and no effect, to the most critical ones, which have both high occurrence rates and severe consequences on the system integrity. 2.4.2. Most critical failure modes Table 2 lists the most critical failure modes defined as those which have a Failure Risk Priority Number (FRPNi ) equal or above 8. This threshold was chosen as the median value of the possible FRPNs deriving from all the possible (ORi , SRi ) pairs (Faure et al., 2016). The most affected sub-systems are at the beginning of the table. Generally, Table 2 is in line with the bibliography (Section 2.3), although the order of magnitude of the figures provides more information. Two sub-systems are more likely to fail: controller and primary transport with both 7 critical failure modes. Secondary transport, storage and solar collection have one critical failure mode each. The most critical failure modes of the external heat exchangers (fouling and other causes of bad efficiency) have a FRPNi of 6. There is therefore no failure mode of this sub-system in the list of the most critical failure mode of Table 2. We conclude from this section that the development of FDD methods should focus on controller and primary transport sub-systems. However, the results of the study have to be analysed qualitatively more than quantitatively. Indeed, they are results of a bibliography and a survey, which are far less accurate than an experimental test or the assembly of a large amount of representative data. For instance, the authors believe that solar collection is also of prime importance although this is not reflected in this study. The choice of one single failure mode to represent numerous causes of solar collector poor efficiency (“produce less energy than expected”) can also explain this result.

3.2. A review of FDD methods applied to LSTS Three reviews about FDD methods applied to LSTS have been published in the last decade (de Keizer et al., 2011; Vanoli et al., 2012; VDI, 2012). Two of them focused on approaches already available on the market or almost ready for the commercialization (Vanoli et al., 2012; VDI, 2012). The third one gathered and described available methods for LSTS, but needs to be completed with new significant works (de Keizer et al., 2011). We also propose to go deeper in analyzing the application and applicability of various FDD methods to LSTS systems, in order to highlight the advantages, drawbacks, challenges,

3. A review of fault detection and diagnosis methods for large solar thermal systems In the previous section, we defined the critical failure modes of a 475

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Table 2 Failure modes with a failure risk priority number equal or above 8. Sub-system

Component

Failure mode

Controller

Solar collector temperature sensor

Wrong measurement No more measurement Wrong measurement No more measurement No more measurement Breakdown Non-optimal control

Heat exchanger input/output temperature sensor Pyranometer Controller

Primary transport

Solar pump

Cause

Never starts

Too low flow Hydraulic connectors Heat transfer fluid (mixture of water and propylene- or ethylene-glycol) Pipes

Leak Bubbles in the heat transfer fluid

Secondary transport

Expansion vessel Pumps

Leak Bad hydraulic balancing Too low pressure Never starts

Storage

Storage tank

Heats less than expected

Solar collection

Solar collector

Produces less energy than expected

parameters • Wrong optimal control algorithm • Non in the system • Air procedure after • Safety overheating causes • Other ageing • Pump • Other causes purge • Wrong • Opened air valves procedure after • Safety overheating causes • Other control • Non-optimal valve • Clogged temperature measurement • Wrong causes • Other • Condensation of vacuum (heat pipes) • Loss Teflon film • Defective mask too large • Solar glass • Broken of the glass • Opacification insulation • Defective • Defective selective coating

ORi

SRi

FRPNi

5 3 4 2 2 2 3

4 5 4 5 5 5 3

20 15 16 10 10 10 9

5

5

25

2

4

8

4 3

3 4

12 12

3 2 2 3

3 4 4 5

9 8 8 15

4

3

12

5

2

10

pioneering methods, called “traditional methods” in the following, and emerging approaches. It turned out that two classes of FDD methods have been used initially because of their ease of development and understanding: expert system and parity space. The other classes belong to emerging tools, which have been studied in recent years. 3.2.1. Traditional FDD methods for LSTS Traditionally, methods for LSTS were developed by people who are not specialized in FDD. This explains why only two classes of methods were explored. These classes correspond to the most intuitive approaches to detect and diagnose faults:

• Expert systems, which are a direct implementation in a computing •

program of the way a human solar technician detects and isolates a fault. Parity space, which consists of the comparison of a model of a normal operating plant with the monitored one.

The following subsections detail the state of the art of both classes. A third subsection is dedicated to several attempts to combine them in a single tool to improve the FDD possibilities. 3.2.1.1. Experts systems in LSTS. An expert system is a computer program that mimics the cognitive behavior of a human expert solving problems for decision making in a specific domain. As all qualitative model-based methods, it employs causal knowledge of the process or system to diagnose faults. Expert systems use expert knowledge to derive a set of if-then-else rules and an inference mechanism that searches through the rule-space to draw conclusions. Expert systems are extensively used in the FDD field, thanks to their ease of development, the simplicity of algorithms, and understanding.

Fig. 3. Different steps of a FDD method.

similarities and differences of the different approaches. In the following subsections, we present a state of the art of the different FDD methods developed for LSTS. Some approaches initially designed for domestic solar plants are also described when they can be easily adapted to larger installations. They are sorted between 476

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Fig. 4. Classification of FDD methods for LSTS.

They are the first kind of methods developed for LSTS. As these first approaches did not have to deal with specific scientific issues, few publications are available (Duff and Millard, 1980; Sun et al., 1999). We can quote the FUKS project (Altgeld, 1999; Mahler et al., 1999) which aimed at developing a complete tool for FDD in SDHW systems. The final tool was an expert system. The whole method did not use mass flow measurement and replaced it with a pressure measurement, which is often more accurate. The IP-SOLAR project (Holter et al., 2012; Ohnewein et al., 2006) was devoted to the development of a complete FDD procedure for large solar application based on an expert, rulebased system. A special emphasis was put on the adaptability of the tool to different hydraulic schemes. Self-learning regression-based algorithms were added to help fault detection. A Swiss project (Bony and Jobin, 2002; Jobin, 2004) which ended in 2004 aimed at integrating and testing an expert system in a controller for domestic and medium installations. The study concluded that developing and maintaining such a FDD system is time consuming and not compatible with the very low profitability of such small-scale plants. Expert systems perform well for diagnosis. In particular, control faults are well detected and diagnosed by current expert systems. But it has the drawback to not be complete, which means that it cannot detect all the possible dysfunctions. Moreover, it requires many sensors to improve its accuracy if it does not employ advanced algorithm. Current approaches use fixed threshold, while adaptive threshold, fuzzy logic and/or probabilistic decisions can improve their robustness and sensitiveness.

Vanoli, 2007, 2006) was developed to assess the performance of the solar system and to be directly integrated into standard control units. The measured daily solar production is compared with the output of a simplified model of the system. The mathematical model was integrated and validated in 12 different solar systems. The mean deviation between measured and expected outputs is less than 10%. ISFH-Input/ Output-Procedure has nowadays commercial applications (INGA, 2012; RESOL, n.d.). This approach is generally only dedicated to fault detection and not to diagnosis because it brings little information about the underlying dysfunction. It often requires a large amount of input parameters, leading to a high parameterization time and a higher probability of modeling errors. The residual typically results from the comparison of a simulated and a measured value of a variable. The analyzed variables are generally the produced solar energy either in primary loop or brought to the user, or the outlet temperature of the solar field. This is in line with our survey on failure modes affecting LSTS which showed that the effect of most of the LSTS faults on the global system is the decrease the solar production (Faure et al., 2016) and led to an estimation of the “Severity Rating” mainly based on this criterion (see Table 1). The monitoring of this production can thus detect all these dysfunctions. Yet no diagnosis can currently be performed with this method alone, which is only able to assess if the system normally performs or not. To improve sensitivity and isolation capacity of parity space methods, algorithms can be developed to compute enhanced residuals like structured or directional residuals (Isermann, 2006) with the limitation that the parity space first needs to be increased by adding new variables (temperature, energy, or power at other points of the system).

3.2.1.2. Parity space methods in LSTS. A parity space method consists of parity equations which are the results of the comparison of the real process behavior with a process model describing the nominal behavior (Patton and Chen, 1992). These equations allow the computation of residuals, which express discrepancies between the real process and the model. If one or more residuals are too large, a fault is detected. Parity space for LSTS is currently extensively used to check performance of an installation and propose contracts with guaranteed solar results (Nielsen, 2014; Peuser et al., 2009; Pradier, 2015; TECSOL, 1999). A design office or a manufacturer guarantees a minimal annual production of a solar plant to a customer. During the first years of production of this plant, simulations of the solar output energy are performed and compared with measurements. Any kind of model can be used to simulate the plant as long as it is physics-based. If the minimal guaranteed amount is not reached, the guarantor has to compensate his client. Such approaches are commercialized since 1988. Experts from International Energy Agency (IEA) SHC Task 45 proposed in 2014 a method to guarantee a power output of a solar plant (Nielsen and Trier, 2014). This power has to be above a guaranteed power under certain boundary conditions corresponding to a full load. The guaranteed power is computed with the help of system’s parameters and boundary conditions. The ISFH-Input/Output-Procedure (Pärisch and

3.2.1.3. Combinations of parity space and expert system in LSTS. Several approaches combine parity space and expert system in order to take advantage of their complementary strengths: parity space is good at detecting faults, while expert system performs better to diagnose. Maltais Larouche and Kummert tested a parity space and an expert system on a collective solar thermal system and concluded that a mix of the two techniques is necessary to reduce the number of false alarms (Maltais Larouche and Kummert, 2016). The parity space is used to detect an abnormal state of the system, whereas the expert system makes the diagnosis of the fault. The University of Kassel conducted the most ambitious project combining parity space and expert system (de Keizer et al., 2008; de Keizer, 2012; Küthe et al., 2011; Shahbazfar et al., 2012; Wiese et al., 2009). The expert system part was designed to detect about 20 failures (de Keizer et al., 2008) (see also Table 3). These include malfunctions of the controller, wrong size of volume flow due to e.g. wrong pump step, embedded air or fouling of hydraulic circuits and malfunctions of the heat exchanger. The parity space part consists of the comparison of a TRNSYS simulation of the solar production of the system to the one obtained with measurements at different time steps 477

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behavior of the process. Process identification-based methods can provide both residuals and estimation of parameters of the monitored system. Almost all of the current methods for LSTS use black-box models (ANNs, linear regression), i.e. models with little model-based knowledge employed in their design, and are devoted to the computation of residuals. Several studies use ANNs to model the solar thermal system. (Kalogirou et al., 2008) simulated temperatures at different points of the system with several ANNs. They proposed several improvements of their method in (Lalot et al., 2008), for instance using only one neuron on the hidden layer and one output neuron to predict the outlet temperatures of the solar field and of the pipes connecting the field to the heat exchanger. Correa-Jullian et al. (2020) compared the performance of different deep learning techniques (ANN, Recurrent Neural Network (RNN), Long Short-Term Memory recurrent neural network (LSTM)) to predict outlet temperature of the solar field. Timma and Blumberga (2013) obtained thermal energies produced by the solar field and the backup heater with a non-linear autoregressive neural network (NARX). Ferreiro Garcia et al. (2014) trained a Neural Networks based Functional Approximation (NNFA) to predict the power of the solar pump. Such models require history data which can be hard to obtain from a real plant: only (Ferreiro Garcia et al., 2014) had access to experimental measures to test their approach. The other authors used synthetic data obtained by simulations of a detailed numerical model (typically TRNSYS) to train and validate their ANNs. Another significant challenge of black-box models is that initial parameterization can be complex as the parameters do not represent physical variables. Gray-box models can be employed to overcome black-box models challenges. They use model-based knowledge to build the structure of the model. Data are then used to fit the parameters of the model. In the FUKS project (Altgeld, 1999), a new linear model estimating heat exchanger outlet temperature from collector outlet temperature and storage temperature was proposed with parameters tuned by regression techniques. During the ISTT project, (Beikircher et al., 1999) improved the annual solar production prediction of their physical model by using historical data to estimate its parameters. Gray-box methods using physics-based embedded model make the initial parametrization easier as parameters represent physical features. Furthermore, those kinds of methods could provide estimated parameters as new features. Estimated parameters are more sensitive to parametric faults (fault that can be simulated by the modification of a parameter of the physical model) than parity spaces and observers (Isermann, 2006). Such features could be used to diagnose for instance the cause of a drop in solar collector energy production or in tank energy storage. 3.2.2.1.2. Classifiers in LSTS. The aim of classifiers is to map the relationship between symptoms and fault classes without a structural knowledge of these relationships. Therefore, they could be seen as a “machine learning expert system”. Some works about LSTS belong to this class. He et al. (He, 2012; He et al., 2012) implemented an Adaptive Resonance Theory (ART) network to detect and diagnose faults by novelty detection. The model is trained with data from a non-faulty system. Then this classifier is fed on line with measurements from the solar system. If a fault occurs, the data do not enter in the normal class anymore and the neural network tries to create a new class. Such attempt is a sign of a fault. Räber as well as Grossenbacher (Grossenbacher, 2003, 1998; Räber, 1997) designed a KNearest Neighbors-like (KNN-like) classifier based on the Fourier transform of the temperature of the global output of the solar field during the minutes which follow the start of the pump. They validated the method with measurements of real systems in normal and faulty behavior and concluded that it successfully detects incipient fault and performs better for faults which imply an inhomogeneous decrease of the performance of the solar field. In the InSun project (InSun, 2015), researchers also tested this method on real data and concluded that it can detect and localize faults within a large solar field, especially those in relationship with performance degradation.

Table 3 Test results of the system expert part of the method developed in Kassel [51]. A plus-minus sign (“ ± ”) means that in some cases failures can be recognized.

Verification of functioning of controller R1 Breakdown of controller R2 Breakdown of sensor R3 Inaccurate sensors R4 False control criteria R5 Inappropriate control scheme R6 Position collector T sensor R7 Inappropriate T/ΔT settings R8 Incorrect sensor position R9 Breakdown of gravity brake Verification of volume flow V1 volume flow too small V1.1 too small pump, wrong pump step V1.2 air in hydraulic circuit V1.3 fouling of hydraulic circuit V1.4 primary pressure in hydraulic circuit too low V2 volume flow too high Verification of heat exchanger performance W1 Dimensioned too small W2 Fouling W3 Hydraulics wrongly connected Verification of collector performance Verification of storage losses

Detection

Identification

yes ± no ± /yes ± ± /yes ± ± yes

no yes/ ± no no no yes no no yes

yes yes yes yes yes

planned no no no no

no ± ± no no

planned planned planned (simulate) (simulate)

(hour, day, month…) (de Keizer, 2012). Uncertainties in measurements and parameterization are taken into account with the definition of a confidentiality range. The method could detect some faults which yield to a diminution of the performances of the solar system but no diagnosis was performed. The goal of the InSun project (InSun, 2015) was to demonstrate the reliability and quality of LSTS for different industrial heat applications. One of the work packages is dedicated to the improvement of FDD abilities for these installations. Within this project, a hybrid approach comprising a state-of-the-art system expert and a comparison between simulated and measured outlet temperature and output energy of the solar field was tested on two plants and successfully detected some faults, but failed to detect all the dysfunctions. 3.2.2. Emerging FDD methods for LSTS Apart from traditional approaches, new methods have been emerging in recent years taking advantage of the amount of available process history data. The fast growing of machine learning approaches led to the development of process history-based methods. Their applications to LSTS are detailed in the next subsection. Observers are another way to use process history data along with expert models. A small subsection is dedicated to them as we show that these methods are relevant for specific cases. 3.2.2.1. Process history-based methods. Process history-based knowledge may be obtained from past experience with the process. Methods using this knowledge are models which use previous measurements of the process to learn their structure and optimize their parameters. These methods can be more automated than the traditional ones so that their adaptation to one particular plant requires less effort. A history-based method can be used either for the feature generation step or for the detection and diagnosis step of the global FDD procedure of Fig. 3. In the first case it generally belongs to identificationbased methods class, in the latter it is a classifier. Note that a same type of model (for instance artificial neural networks (ANNs)) can be used for both purposes. Readers who would like more information about process history-based methods for FDD can refer to Isermann (2006). 3.2.2.1.1. Identification-based methods in LSTS. Identification-based methods are process history-based methods dedicated to features generation. They enable to construct a model, which mimics the 478

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Malenkovic, 2012) concluded that an effort is still required to push more FDD concepts from the R&D level to market ready FDD systems. As a global assessment, there is currently a predominant use of expert system and parity space to address the problem of FDD for LSTS, thanks to their ease of development, the simplicity of operators and user friendliness. There are also some studies on process-history based approaches but still in a research phase. The main drawback of such methods for LSTS is the duration of the learning phase. This is especially true for methods which use little model-based knowledge (i.e. black-box methods) and which represent almost all the presented applications: these methods assume a learning phase of at least three seasons and generally one year without faults. However, (Lalot et al., 2008) proposed a method with a learning phase of less than two days, which suggests that the learning phase could be dramatically reduced.

Previously listed applications for LSTS only attempt to use classifiers as a fault detector: only one normal class is learned and if the current state does not belong to this class, a fault is detected. In fact, more classes could be created which could enable isolation of a special dysfunction (one class per fault). A first attempt in this direction is proposed by (Jiang et al., 2019), who proposed a fault diagnosis model based on a collection of Support Vector Machine (SVM) models to extract symptoms and Dempster-Shafer theory to derive the final decision. They successfully classified four faults using experimental data from a test bench. This method looks promising but more research is needed to analyze reproducibility, robustness and possibility of extension of the method to other faults. Liu et al. (2015) presented a technique to diagnose faults using a Bayesian network once symptoms are generated. This approach overcomes the problem of incomplete data or expert knowledge but must be coupled with another method. Moreover, to obtain sufficiently representative classes, a reasonable amount of historical data for each fault and for several boundary conditions is still required. These data are often not available from measurements on real installations.

4. Performance evaluation of the FDD methods for LSTS In the previous section, we presented the main methods of FDD for LSTS and highlighted their general strengths and weaknesses. We will now evaluate more precisely the performance of these applications and their potential. The performance of a FDD algorithm can have several definitions. One of the most general is proposed by (Venkatasubramanian et al., 2003): a set of desirable characteristics for a FDD methods was listed, which encompasses ability to detect accurately faults but also practical requirements as storage and computational needs. As a first step, we then propose to evaluate the current FDD applications along a reduced set of these desirable characteristics adapted to LSTS. In a second part, we will focus on the current and potential ability of each class of FDD methods to detect and accurately diagnose the critical faults of a LSTS defined in Section 2.4.2.

3.2.2.2. Observers in LSTS. Observers are specific extensions of statespace models designed to track a state and to be insensitive to unknown inputs like process and measurement noise or modeling uncertainties. One of their main advantages is that they have a short learning phase. Several forms of observers are especially suitable for FDD: Kalman filters (Tudoroiu et al., 2009), output observers (Isermann, 2006), sliding mode observers (Spurgeon, 2008). Observers are today rarely used for LSTS. Only (Kicsiny and Varga, 2013) proposed a real-time nonlinear global state observer design. Although the results were promising, other methods can perform better for the application case presented. Nevertheless, observers showed significant contribution for specific aims like leak location (Verde and Torres, 2015) which could be further studied.

4.1. Desirable characteristics Among the extensive list of desirable characteristics of a FDD system proposed by (Venkatasubramanian et al., 2003), we identified the most significant for LSTS, which are:

3.3. Summary about current FDD methods for LSTS

• Completeness: the fraction of faults that can be detected by a

Table 4 summarizes the main current methods for LSTS and lists some characteristics of these methods. Only some research projects involving expert systems and parity space led to commercialized applications, and a report from the Qaist project in 2012 (Vanoli and

method, among all the possible faults. This feature evaluates the detection part of a FDD algorithm (see Fig. 3).

Table 4 Comparison of FDD methods applied to solar thermal systems. Name

Type

Date of last publication

Progress

System size

FUKS (Altgeld, 1999; Mahler et al., 1999) IP-SOLAR (Holter et al., 2012; Ohnewein et al., 2006) IEA power (Nielsen and Trier, 2014) IOC (Pärisch and Vanoli, 2007, 2006; VDI, 2012)

Expert system (+Identification) Expert system Parity space Parity space

1999 2012 2014 2010

Small (scalable) Large Large Any

GRS (Nielsen, 2014; Peuser et al., 2009; Pradier, 2015; TECSOL, 1999) Kassel (de Keizer et al., 2008; de Keizer, 2012; Küthe et al., 2011; Shahbazfar et al., 2012; Wiese et al., 2009) InSun (InSun, 2015) Kalogirou et Lalot (Kalogirou et al., 2008; Lalot et al., 2008) Correa-Julian (Correa-Jullian et al., 2020) Timma et Blumberga (Timma and Blumberga, 2013) Ferreiro Garcia (Ferreiro Garcia et al., 2014)

Parity space

2013

Expert system + parity space

2015

Commercial R&D Commercial Commercial, normative Commercial, normative R&D

Expert system + parity space Identification (ANN) Identification (ANN-RNN-LSTM) Identification (NARX) Identification (NNFA) + expert system Identification (TRNSYS physical model) Classifier (KNN-like)

2015 2008 2020 2013 2014

Ready to market R&D R&D R&D R&D

Large Any Any Any Any

2012

Normative

Large

2015

R&D

Any

2012 2019 2013

R&D R&D R&D

Small (scalable) Any Any

ISTT (Beikircher et al., 1999; VDI, 2012) Grossenbacher (Grossenbacher, 2003, 1998; InSun, 2015; Räber, 1997) He (He, 2012; He et al., 2011, 2012) Jiang (Jiang et al., 2019) Kicsiny et Varga (Kicsiny and Varga, 2013)

Classifier (ART) Classifier (SVM) Observer (real-time nonlinear global state observer)

479

Large Any

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? =

? ? – – – – – ? –

• •

+ + – – – = – – ?

•

= + – – + – + – – + =

4.2. Critical faults detection and diagnosis ability After a global overview of the performances of current FFD applications for LSTS, we now focus on the ability of FDD classes to detect and diagnose faults leading to the critical failure modes defined in Section 2.4.2. This study enables us to review state of the art but also to analyze the possible developments of each class in a near future. In the following, the first subsection presents the applied methodology while the second one describes the results. 4.2.1. Methodology From Table 2, we derived the critical faults, which can be either the failure modes themselves, either their causes, depending on what the authors think that should be detected and isolated by an ideal FDD method, based on their expert knowledge. Table 6 summarizes these faults. For the sake of clarity, an acronym is assigned to each fault which is built as following: first letter stands for the sub-system, second one for the component, third one for the failure mode and forth one for the special cause if any. The different acronyms are indicated in the

Classifier

Observer

= + – ++ – – – – ++ = Identification

Expert system + Parity space

Parity space

+ + + ++ = ++ + + – +

+ + + = + + + ++ – – – + + + ++ + ++ +

FUKS (Altgeld, 1999; Mahler et al., 1999) IP-SOLAR (Holter et al., 2012; Ohnewein et al., 2006) IEA Power (Nielsen and Trier, 2014) IOC (Pärisch and Vanoli, 2007, 2006; VDI, 2012) GRS (Nielsen, 2014; Peuser et al., 2009; Pradier, 2015; TECSOL, 1999) Kassel (de Keizer et al., 2008; de Keizer, 2012; Küthe et al., 2011; Shahbazfar et al., 2012; Wiese et al., 2009) InSun (InSun, 2015) Kalogirou et Lalot (Kalogirou et al., 2008; Lalot et al., 2008) Correa-Julian (Correa-Jullian et al., 2020) Timma et Blumberga (Timma and Blumberga, 2013) Ferreiro Garcia (Ferreiro Garcia et al., 2014) ISTT (Beikircher et al., 1999; VDI, 2012) Grossenbacher (Grossenbacher, 2003, 1998; InSun, 2015; Räber, 1997) He (He, 2012; He et al., 2011, 2012) Jiang (Jiang et al., 2019) Kicsiny et Varga (Kicsiny and Varga, 2013) Expert system

Completeness

recommendations and to explain why certain hypotheses are proposed and why the others are rejected, all in a language understandable by the human operator. Adaptability: the ability of the tool to adapt to a change in the monitored system during the lifespan of the plant (extension of the plant, modification of the energy demand, replacement of components…) and be improved as more experience and more information become available. Ease of implementation: fast deployment is leveraged by simple models and a limited amount of historical data. Multiple fault identifiability.

Based on these definitions, Table 5 presents the performances of available methods presented in Section 3.2. A scale from (−−) or (−) to (+) or (++) reflects how much the different characteristics are fulfilled. (=) means an average performance. (?) is used when not enough information is available to evaluate the criteria for this method. The table was filled thanks to the literature and author’s expert knowledge. The authors are aware that this analysis is partially subjective due to the lack of objective and quantifiable metrics. To obtain this kind of metrics, a full benchmark of the methods over the same dataset would be necessary, which is left to future studies. However, the authors think that this simple analysis is able to highlight the main characteristics, strengths and weaknesses of each method. Venkatasubramanian et al. (2003) noted that there is a trade-off between completeness and resolution. Indeed, a good resolution generally requires that the set of faults addressed by the method is as minimal as possible, which is in contradiction with a good completeness. We can conclude from Table 5 that current applications for LSTS generally favor completeness rather than resolution. The reason is that they are designed to be used alone. Available applications of historybased methods suffer from a complex implementation. Moreover their adaptability is questionable due to their requirement of a high amount of data for training, except for Kalogirou and Lalot’s method. One can assume that a re-train of the models after a change in the plant will require less data but no study has been conducted in this direction. So the question remains open. The explanation facility of all approaches is quite good because of the self-explanatory content of the exposed symptoms, for instance “too low solar production”. However multiple fault identifiability cannot be performed by algorithms using only these features. In the case of multiple features, no tests were done to verify whether the algorithms could indeed detect all the features. Finally, no current method fulfills all the desirable characteristics.

++ + ? ? ? + ? ? ?

= – + + = –

= = + = + –

? ? – – – ?

• Resolution: the capacity of accurately isolating one fault, which is one of the goals of the diagnosis part of the algorithm. • Explanation facility: the ability, for a FDD system, to justify its

Name Class

Table 5 Performance of the current FDD methods along several criteria.

Resolution

Explanation facility

Adaptability

Ease of implementation

Multiple fault identifiability

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Table 6 Assigned acronyms to each critical fault. Sub-system

Component

Fault

Code

Controller

Solar collector temperature sensor

Wrong measurement No more measurement Wrong measurement No more measurement No more measurement Breakdown Non-Optimal control Never starts (Air in the system) Never starts (Safety procedure after overheating) Never starts (Other causes) Too Low flow Leak Bubbles in the heat transfer fluid Bad hydraulic balancing Too low Pressure Never starts (Safety procedure after overheating) Never starts (Other causes) Heats less than expected Produces less Energy than expected

CSW CSN CHW CHN CPN CCB CCO PPNA PPNS PPNO PPL P-L PFB PCB PVP SPNS SPNO TTH OSE

Heat exchanger input/output temperature sensor Pyranometer Controller Primary transport

Solar Pump

Secondary transport

Hydraulic connectors / Pipes Heat transfer Fluid Pipes Expansion Vessel Pumps

Storage Solar collection

Storage Tank Solar collector

by the fact that development was historically more focused on small plants for which advanced diagnosis is too expensive and non-necessary. This is different for larger installations in which localization can be a major factor to reduce time and cost of repair, and early detection can prevent substantial financial losses. Fig. 6 shows that even with the proven possibilities of improvement discussed previously (enhanced residuals for parity space in Section 3.2.1.2 and adaptive threshold for expert system in Section 3.2.1.1), traditional methods will not be sufficient to detect and diagnose all the critical faults. On the other hand, emerging methods have the potential to at least detect all the critical faults. Moreover, these new methods show interesting contributions concerning non-detected faults and improvement of diagnosis. Main contributions are:

table and the used letters are in bold red. A ranking number is defined to assess the ability of a method j to detect and diagnose a fault i : the FDD ability Dij . The value of Dij is between 0 and 4 and is estimated with the help of the criterion defined in Table 7.

4.2.2. Results The FDD ability of different kinds of methods is determined along the criteria Dij with the help of the bibliography of the previous parts of this paper and expert knowledge. The results are presented in Figs. 5 and 6. Fig. 5 shows the current abilities of the traditional methods: expert system and parity space. Fig. 6 is a projection of the possible abilities of the two main classes of approaches previously defined: traditional methods (Section 3.2.1), and history-based and observerbased methods, i.e. emerging methods (Section 3.2.2). Again, this study is partially subjective, as the criteria score is based on author’s expert knowledge and literature review. Yet we assume we captured at least the main tendencies of each class of methods. As we saw in the Section 3.2.1.2, parity space can successfully detect faults which lead to a significant decrease of the solar production. Its maximal FDD ability (2, i.e. detection and determination of affected sub-system) is achieved when the approach comprises a detailed model of each sub-system. Yet current methods cannot give information about the detected fault. Expert systems perform better in terms of isolation but they are not capable of localization or of early detection. The former requires more sensors (particularly in the solar field); the latter is in generally done with the help of historical data. The combination of the two methods would lead to better results but some faults remain undetectable: bad hydraulic balancing (PCB) and bubbles in the heat transfer fluid (PFB) (which is in particular a cause of bad hydraulic balancing). Moreover, bad production of solar collector (OSE) and nonoptimal control (CCO) cannot be isolated, even approximately. We can also note that no advanced diagnosis is possible. This can be explained

• better isolation of a non-optimal control (CCO) or bubbles in the • •

One must yet be careful not to conclude from Fig. 6 that emerging methods can be used alone. Indeed, we showed in a previous study (Faure et al., 2016) that almost half of the causes of faults are present at the commissioning of the installation. Their detection and diagnosis therefore require the use of methods which do not need a learning phase such as parity space and expert systems. Moreover, it could be interesting to add control tools such as the one proposed by (Mezni et al., 2017) during the design phase to prevent economic losses. The future FDD approaches for LSTS will probably look like an assembly of different kinds of methods.

Table 7 Definition of the value of FDD ability criteria to evaluate the ability of the different methods to detect and diagnose each critical fault. Dij

Criteria

0 1 2 3 4

Fault non detected Detection without diagnostic Detection and approximate isolation (determination of affected sub-system) Detection and accurate isolation (determination of the fault) Localization or early detection

transfer fluid (PFB), the latter using an algorithm on the pressure measurement. The identification of the cause of the poor operation of solar collectors (OSE) or storage tanks (TTH) seems also possible to the authors with appropriate methods. localization of some faults such as bad hydraulic balancing (PCB), leak (P-L) and faulty solar collectors (OSE) with the help of methods derived from (Grossenbacher, 1998) as demonstrated by the project InSun (InSun, 2015). In the same way, localization of a faulty storage tank (TTH) could be probably achieved. early detection of leaks (P-L) with the help of ANN (Tassou and Grace, 2005) or observers (Delgado-Aguiñaga et al., 2016; Verde and Rojas, 2015). Some studies suggest that regression techniques and observers can detect a risk of pump breakdown (PPNA, PPNO) (Dalton et al., 1995; Isermann, 2011; Kallesøe, 2006).

5. Conclusion In this paper, we surveyed faults which can affect LSTS and the 481

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Fig. 5. FDD abilities of current commercialized methods for LSTS. Bar colours correspond to the affected sub-system as in Fig. 1: solar collection (yellow), controller (grey), primary transport (green), storage (dark blue), secondary transport (light blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

To achieve these goals, we propose the following actions:

current ways to detect and diagnose them. After a brief description of the system, we presented a literature review of the system dysfunctions and a study to assess these dysfunctions. It enables us to define a set of critical failure modes and to highlight the sub-systems which most require FDD: primary transport, controller and solar collection. Then we described the state of the art of the research concerning FDD methods for LSTS. We showed that there exists few commercialized applications and improvements are still needed in particular concerning processhistory based methods. Finally, we evaluated current and potential methods for FDD following two approaches. In a first analysis, we introduced six desirable characteristics of a FDD method for LSTS and evaluated the performance of the current algorithms along them. We showed that currently no method fulfills the specifications of an ideal FDD method for LSTS. Second, we analyzed the ability of FDD methods to detect and diagnose the critical faults, i.e. faults that lead to critical failure modes. We identified the possibilities of improvements of each class of algorithms and demonstrated that potentially all the critical faults could be at least detected in a near future. Current FDD methods for LSTS mainly derive from the research on small thermal solar plants. It explains why the remaining non-detected faults are specific to large installations: bad hydraulic balancing and bubbles in the heat transfer fluid. In fact, this last fault exists in domestic systems but tends to be directly detected by the consumers because of the noise of the pump. The lack of attention for diagnosis also comes from this history: isolation and localization were not considered critical in the past. Today, LSTS are developing and become larger and larger (Bava et al., 2017). Therefore, the FDD tools need to be designed specifically to meet the challenges of large scale installations. By order of priority, research should focus on:

1. Development of process history-based methods: the use of history data will help fault isolation and localization. We highlighted in Section 3.2.2.1 some ideas: process identification with a physical model, classifiers with multiple fault classes. Moreover, the quasi cyclic daily and yearly behavior of both available solar resource and load demand suggests that pattern recognition techniques should be considered. To develop this kind of approach, the problem of the amount of data required for the initial learning phase as well as for a re-training after a change in the plant should be first addressed. 2. Design of observers for specific aims, which cannot be achieved by other methods. We propose to study observers in a second phase, first because their development is time expensive, and second because observers are generally dedicated to one particular application. 3. Improvement of traditional methods. The potential of improvement seems slight for these well-known approaches but we identified some avenues: generating enhanced residuals (Isermann, 2006) to improve sensitivity and isolation capacity of parity space methods; using adaptive, probabilistic or fuzzy thresholds in expert systems. This can also improve the sensitivity of all the methods. 4. Combination of methods. Final tools will include several algorithms. The general architecture and the way the different methods interact with each other should also be studied.

• •

A benchmark test could be also developed to compare performances of the different methods along the same dataset.

1. detection of all the faults: future works should in particular focus in finding a way to detect a bad hydraulic balancing and bubbles in heat transfer fluid. 2. diagnosis with a focus on localization. The authors think that isolation techniques could be improved by working on faults signatures (i.e. specific modification of system behavior due to the fault) as (Tsanakas et al., 2016) did.

Declaration of Competing Interest All authors have no actual or potential conflict of interest including any financial, personal or other relationships with other people or organizations within three years of beginning the submitted work that could inappropriately influence, or be perceived to influence, their work.

Fig. 6. Maximum estimated FDD abilities of different methods for LSTS. Bar colours correspond to the affected sub-system as in Fig. 1: solar collection (yellow), controller (grey), primary transport (green), storage (dark blue), secondary transport (light blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 482

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