- Email: [email protected]
Knowledge-based decision support for the improvement of standard products
CIRP Annals - Manufacturing Technology 62 (2013) 159–162
Contents lists available at SciVerse ScienceDirect
CIRP Annals - Manufacturing Technology jou rnal homep age : ht t p: // ees .e lse vi er . com /ci r p/ def a ult . asp
Knowledge-based decision support for the improvement of standard products Michael Abramovici *, Andreas Lindner Information Technology in Mechanical Engineering (ITM), Ruhr University, Bochum, Germany Submitted by Frank-Lothar Krause (1), Berlin, Germany
A R T I C L E I N F O
A B S T R A C T
Keywords: Product development Decision making Knowledge based system
Throughout the last couple of years several approaches for the feedback-based improvement of capital goods have been developed. Based on previous research work the authors are proposing a new methodology of decision support for the improvement of existing mass-produced standard products. This approach is based on prescriptive decision theory and uses feedback data in addition to product-specific characteristics and properties. For the prediction and evaluation of different improvement alternatives, the presented solution uses object-oriented Bayesian networks (OOBN). The validation of the proposed solution has been demonstrated on the basis of decision processes for the improvement of centrifugal pumps. ß 2013 CIRP.
1. Introduction The majority of industrial goods are mass-customized products realized by the configuration of standardized modules like bearings, axes, and pumps. Although these components are mature products, they are almost always subject to periodical improvements driven by customers, competitors, users, service providers, new legislative regulations, or technologies. Nevertheless, product design research activities mainly address methods and tools for the development of new products [1]. In the past, only few research activities addressed the acquisition and provision of product use feedback information for the improvement of future product generations [2–4]. Until now, however, the support of specific design decisions for the improvement of standard products has been considered only scarcely. Due to the lack of appropriate methods and tools, the improvement of existing standard products in current industrial practice is chiefly made individually, subjectively, and not systematically. This often leads to suboptimal or over-engineered products. The increased availability of digital information about current product generations, as well as progresses in decision theory offer new opportunities to optimize the improvement process of standard products. Therefore the solution proposed in this paper adapts and extends existing general design theories and methods and offers a framework for the product improvement process by exploiting available product use information. 2. Methodical approach 2.1. Product focus The proposed solution considers standard mass-produced products with a long lifetime and a variety of applications and
* Corresponding author. E-mail addresses: [email protected], [email protected] (M. Abramovici). 0007-8506/$ – see front matter ß 2013 CIRP. http://dx.doi.org/10.1016/j.cirp.2013.03.076
operational environments. It was assumed that these products are improved periodically, and that they are equipped with embedded micro-sensors capturing use information (e.g. operational temperature and rotation speed) mainly for condition monitoring purposes. A further assumption was the availability of large sets of product use data for analysis tasks. 2.2. Data sources and data types The new decision support solution chiefly uses data about the use of the current product generation that can be provided by customers and service providers [5]. That information contains technical product sensor data (e.g. work load), operational environment data (e.g. temperature), operational data (e.g. use time), product service data (e.g. failures), or product user data (e.g. use of specific functions). That product use data can be acquired from condition monitoring or service databases, customer relationship management systems, or Internet portals. That data can be objective or subjective, structured (e.g. sensor data) or unstructured (e.g. texts). The proposed solution focuses on objective, structured data about the product use, the product operational environment, and service events. 2.3. Decision support framework The developed decision support framework for the improvement of standard products is described in Fig. 1. The drivers for product improvements are product failures, deviations in product performance compared to the expectations, new similarly better or cheaper competitive products, new emerging technologies which could substitute current ones, as well as new relevant laws and regulations which affect existing products. For these reasons new product improvement requirements must be defined. These requirements extend the existing product specification.
160
M. Abramovici, A. Lindner / CIRP Annals - Manufacturing Technology 62 (2013) 159–162
Fig. 1. Decision support framework for the improvement of standard mass-produced products.
The product improvement process should follow all or some of the same stages as within the process of new product development, including requirement analysis, functional, principle, and embodiment design. Compared to the development process of new products the product improvement process occurs within very strong boundaries and constraints. The main existing product specifications (requirement lists) as well as the basic functional, principle, and embodiment product structures are already set. The product developer can only modify, substitute, extend, or cancel a few items within these structures. These changes require a lot of difficult decisions. Some examples of such decisions arising during the product improvement process are: Will the new requirements for product improvement affect the existing product specifications? Does the new product specification affect the existing functional, principle or product structures? Is a cancelation, substitution, modification, or extension of existing items necessary? If so, what will be the consequences or the impact of each item change on the product functions, features, and performance? In our approach the development process of improved products is regarded as a sequence of iterative decision cycles, considering an uncertain product operational environment and many decision optimization criteria. In the last decades a variety of general decision theories and methods have been developed and implemented in different application areas [6]. The solution proposed in this paper adapts and extends the rational, prescriptive, multi-criteria decision theory under uncertainty, as well as several decision support methods to assist relevant operative decisions in the improvement of standard mass-produced products. 2.4. Reference decision making process Based on classical prescriptive decision theory the following reference process has been developed for each decision cycle in product improvement. A. Problem analysis Problem identification Problem diagnosis (cause detection) Definition of improvement objectives (decision criteria) B. Generation and selection of solution alternatives Generation of solution alternatives Evaluation of solution alternatives Selection of a solution alternative C. Implementation of the selected solution alternative Product design change Product improvement prototyping, realization and evaluation Improved product operation, monitoring, and evaluation
The focus of the developed decision support framework covers the first two decision stages (A and B). The following section describes the specific methods supporting the different tasks of these two decision making stages. A more detailed description based on a validation sample will be made in Section 5.
3. Decision support methods 3.1. Problem analysis methods A problem in product use occurs if the product or modules of a product fails, or if the deviation of the product performance compared to its requirements exceeds the expected values. For problem identification classical statistical analysis methods have to be applied, e.g. investigating product use data or service data distribution (e.g. failure frequency, maintenance cycles, and workload). The second step in the decision process is problem diagnosis, namely the detection of the reasons which caused the problem. For example, problem causes can be an unfavorable product operational environment (e.g. an exceeded operational temperature) or a higher product workload than originally expected. These parameters cannot be changed by the product developer. In contrast to these operational and environmental use profiles, the product designer can optimize other design parameters like product type, material, dimensions, or shapes in order to improve the product. Problem diagnosis tasks include the identification of any mentioned unchangeable parameters as well as that of changeable product features, which cause the identified problems. For the problem diagnosis fault tree diagrams and/or ‘‘What-If’’ [7] analyses can be used [6]. The analysis of past product use data can show the probabilistic value distribution of the product operation environment. The influence of these distributions on problem events (e.g. on a product failure) can be calculated by a fault tree analysis based on Bayesian networks (BN). Bayesian Networks are oriented graphs, modeling events (e.g. product failures) and related influence factors modeled as nodes and their dependencies as directed edges. The probability distribution of different value intervals of each node (influence factor) is described by a conditional probability table (CPT). Inference algorithms based on these BN calculate the probable impact of the influence factors on other factors or on event nodes (e.g. probability of a failure). The use of traditional BN for problem diagnosis of standard products has been already used and described in a previous paper by the same authors [3]. This application considered unchangeable operation environment parameters only. In order to include an investigation of the impact of changeable product features on problem occurrence, object-oriented Bayesian networks (OOBN) have been chosen [8]. These OOBN and their related inference algorithms are similar to the classical BN but also allow the additional consideration of changeable product parameters (e.g. material and friction) and calculate their probable influence on problem occurrence (Fig. 2).
M. Abramovici, A. Lindner / CIRP Annals - Manufacturing Technology 62 (2013) 159–162
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traditional decision making methods (e.g. pairwise comparison) have been implemented. 5. Methods and tool validation
Fig. 2. Comparison of the used BN types.
In contrast to traditional BN the used OOBN cannot be learnt from product use data. The rough layout of the OOBN is given by the BN (cf. Section 2.2), but nodes describing product features must be added by the product developer. The CPTs of those additional nodes are assigned by the designer, based on service data not regarded in the initial BN. The third step of the problem analysis stage is the definition of improvement objectives, resulting from the extension of the initial product specification by the improvement requirements (e.g. longer module lifetime, shorter maintenance cycles). As the defined improvement objectives are not of equal importance, the product developers have to assign weights to each requirement/objective item. The new product specification is the main source for defining decision criteria for the evaluation of alternatives.
The use of the developed approach, methods, and tool prototypes have been validated by decision processes for the improvement of centrifugal pumps (Fig. 3). These products fulfill all the assumptions mentioned in Section 2.1. The functions, principles, structure, and components of centrifugal pumps, their operational environment, as well as the monitored product use data have been described by the authors in [5]. The monitored data of a large number of centrifugal pumps showed deviations of the pump’s behavior compared to the expectations defined in the existing product specification. That was the reason for initiating a product improvement project. The improvement process has been carried out by using the approach, methods, and software prototype described in Sections 3 and 4.
3.2. Methods for the generation and selection of alternatives The generation of alternative solutions to solve existing problems is a creative process that can only be supported by brainstorming or innovation techniques (e.g. TRIZ) [1]. For that product developers can use their experiences and different, relevant, available solution catalogs. In a next step the created alternative solutions have to be evaluated against the decision optimization criteria. For this evaluation classical quantitative methods (e.g. cost benefit analysis), qualitative methods (e.g. strength, weaknesses, opportunities, and threats analysis), or combined methods (e.g. analytic hierarchy process or pairwise comparison) [6] can be used. The result of using these general methods is a ranking of the considered alternative solutions, which can be the basis for a preselection of alternatives. In order to make a final decision, an assessment of the impact of the preselected alternatives on the original detected problems can be performed. For this consequence analysis the OOBN and the related inference algorithms described in Section 3.1 can be used for a ‘‘What if’’ analysis. Within this analysis the product developers can change several values of the CPT for some of the changeable influence nodes and check the probable influence on events (e.g. failure). Upon successful checking of all of the influence nodes a final selection of a product improvement solution alternative can be made. 4. Software prototype implementation The proposed decision making process and methods have been implemented in a software prototype. This prototype uses an open source data warehouse system (PENTAHO) for the acquisition, storage, and management of all product use information, as well as an open source knowledge representation and inference environment (WEKA). The knowledge-oriented software allows the representation of the specific object-oriented Bayesian network (OOBN) models and supports impact or ‘‘What If’’ analyses. The backbone of the data warehouse data model is the product type structure (Bill of Material), which is imported from the Product Data Management System Windchill (by PTC) and entered use data collected from all of the monitored product instances. In addition some open-source software modules supporting
Fig. 3. Centrifugal pumps as product type validation sample.
5.1. Problem analysis An analysis of the statistical distribution of component failures and deviations of the expected pump performance parameters led to the identification of critical parts. In the considered sample one of the pump bearings failed and had to be maintained and changed more often than expected. Hence, this pump bearing had to be improved. For the problem diagnosis an OOBN analysis as described in Section 3.1 was applied. First a BN has been learnt using aggregated data captured from several pump instance working space sensors (e.g. temperature), as well as operational conditions (e.g. workload, rotation speed). As these parameters cannot be influenced by the product developer, it is possible to assume that the same retrospective distribution will occur during the future use of an improved product. Then nodes describing product features and their CPTs were added by the product developer. Hence the possible influencing factors on the bearing failure were identified and displayed in a fault tree (OOBN). A change of the product design parameters (e.g. material, limitation of the rotation speed, or a change of the lubricant parameters) and the use of the OOBN inference algorithms showed that a change of the bearing features or type under the same product operation conditions can significantly reduce the bearing failure probability. In the following the requirements for the bearing improvement were defined. Considering the main identified problems the main improvement objective was to expand the lifetime of the product by extending service intervals and the mean time between failures and by maintaining or improving all of the characteristics described in the initial product specification. The new product specification has been generated from the improvement
M. Abramovici, A. Lindner / CIRP Annals - Manufacturing Technology 62 (2013) 159–162
162
requirements/objectives and from the initial product specification. Then, improvement objectives have been weighted according to their importance. 5.2. Generation and selection of alternatives The problem identification and diagnosis indicated a necessity to change the features of one pump bearing. The first considered alternatives were the change of the bearing dimensions or characteristics. Due to dimensional constraints the only option was to change the bearing features by substituting the existing bearing (ball bearing) with another bearing type with the same dimensions. The considered alternatives were a groove, a cylinder, a roller, or a journal bearing. Their characteristics have been chosen from standard product catalogs. The preselection of alternative solutions was made by using the defined weighted improvement objective list and a pairwise comparison of the alternative solutions with the existing solution (Table 1). The weighted sum of the assessed rating points led to a ranking of the chosen alternatives. According to the ranking, the alternative with the highest total score, the roller bearing was chosen for further evaluation. Table 1 Exemplary use of the pairwise comparison method for ranking decision alternatives. Improvement objectives (decision criteria)
Weight
Groove bearing
Sum of weighted rates (%) Ranking:
populations of product use data sets have been created by random generators. Nevertheless the rapid proliferation of low-cost product-embedded micro-sensors as well as the increasing control of product operation by the product providers within emerging product-service offers dramatically enhance the availability of product use data, which can be exploited for the improvement of future product generations. 6. Conclusion and outlook
Cylinder bearing
Roller bearing
Journal bearing
SR
WR
SR
WR
SR
WR
SR
WR
1 3
8 54
2 3
16 54
4 3
32 54
2 4
16 72
4
24
4
24
4
24
5
30
.. .
.. .
.. .
.. .
.. .
.. .
.. .
.. .
9
4
36
4
36
5
45
3
27
14
5
70
3
42
3
42
2
28
2 .. .
5 .. .
10 .. .
5 .. .
10 .. .
4 .. .
8 .. .
1 .. .
2 .. .
100
326
314
426
264
2
3
1
4
Existing requirements Reliability (>90%) 8 Mean time to 18 repair (<1 h) 6 Installation costs (<200s) .. .. . . New requirements Long lifetime (>10.000 h) Mean time between failures (>30d) Disposal cost (<50s) .. .
Decision alternatives
Fig. 4. Exemplary use of an OOBN for the failure prediction of a ‘roller bearing’ within a centrifugal pump.
Fulfillment compared to the existing solution: 1–2: worse; 3: equal: 4–5: better. SR: simple rates; WR: weighted rates.
For a verification of the chosen alternative solution a prediction of the behavior of this solution can be made by adapting the OOBN generated in the problem diagnosis stage. The value distribution of the operation environment parameters in this OOBN cannot be influenced by the product developer. The product feature node distribution values, however, can be changed to calculate the probability of a bearing failure when using a different bearing type (here: roller bearing). In the above example the product developer can use the knowledge-based inference software by carrying out a ‘‘What If’’ analysis (Fig. 4). According to that analysis the assessment of the new product features of the roller bearing leads to a very low failure probability. Hence this solution has been chosen and will be implemented. The validation of the developed methods and tools confirms their feasibility and shows their potential of enhancing decision processes for the improvement of standard products. Unfortunately a major problem when using these methods is the still low availability of product use data. For validation purposes large
The presented solution has a high potential to enhance decision processes for the improvement of standard mass-produced products. The proposed methodology extends and adapts general prescriptive decision theory and decision methods, and exploits sensor and operational data from the use of previous product generations. Future research work will consider additional data sources (e.g. customer, service, user databases), further data types (e.g. unstructured or semi-structured data), and will focus on the development of further new analysis, diagnosis, and decision making methods, as well as on the improvement of visualization techniques. Acknowledgements We express our sincere thanks to the German Research Foundation (DFG) for financing this research within the project ‘‘Product Lifecycle Management Extension through KnowledgeBased Product Use Information Feedback into Product Development’’.
References [1] Pahl G, Beitz W, Feldhusen J, Grote K-H (2007) Engineering Design – A Systematic Approach, Springer Verlag, London. ISBN: 978-3540199179. [2] Kiritsis D (2011) Closed-loop PLM for Intelligent Products in the Era of the Internet of Things. Proceedings of Computer-aided Design, 479–501. [3] Abramovici M, Lindner A (2011) Providing Product Use Knowledge for the Design of Improved Product Generations. CIRP Annals – Manufacturing Technology 60(1):211–214. [4] Takata S, Kimura F, van Houten F, Westkaemper E, Shpitalni M, Cegarek D, Lee J (2004) Maintenance: Changing Role in Life Cycle Management. CIRP Annals – Manufacturing Technology 53(2):643–655. [5] Abramovici M, Lindner A, Walde F, Fathi M, Dienst S (2011) Decision Support for Improving the Design of Hydraulic Systems by Leading Feedback into Product Development. Proceedings of the 18th International Conference on Engineering Design (ICED), Copenhagen, 1–10ISBN: 978-1904670322. [6] White DJ (2009) Decision Theory, Aldine Pub. Co., Chicago. ISBN: 9780202308982. [7] Lutters D, Vaneker T, van Houten F (2004) ‘What-If’ Design: A Synthesis Method in the Design Process. CIRP Annals – Manufacturing Technology 53(1):113–116. [8] Koller D, Pfeffer A (1997) Object-Oriented Bayesian Networks. Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence, Providence, RI, 302–313.
Contents lists available at SciVerse ScienceDirect
CIRP Annals - Manufacturing Technology jou rnal homep age : ht t p: // ees .e lse vi er . com /ci r p/ def a ult . asp
Knowledge-based decision support for the improvement of standard products Michael Abramovici *, Andreas Lindner Information Technology in Mechanical Engineering (ITM), Ruhr University, Bochum, Germany Submitted by Frank-Lothar Krause (1), Berlin, Germany
A R T I C L E I N F O
A B S T R A C T
Keywords: Product development Decision making Knowledge based system
Throughout the last couple of years several approaches for the feedback-based improvement of capital goods have been developed. Based on previous research work the authors are proposing a new methodology of decision support for the improvement of existing mass-produced standard products. This approach is based on prescriptive decision theory and uses feedback data in addition to product-specific characteristics and properties. For the prediction and evaluation of different improvement alternatives, the presented solution uses object-oriented Bayesian networks (OOBN). The validation of the proposed solution has been demonstrated on the basis of decision processes for the improvement of centrifugal pumps. ß 2013 CIRP.
1. Introduction The majority of industrial goods are mass-customized products realized by the configuration of standardized modules like bearings, axes, and pumps. Although these components are mature products, they are almost always subject to periodical improvements driven by customers, competitors, users, service providers, new legislative regulations, or technologies. Nevertheless, product design research activities mainly address methods and tools for the development of new products [1]. In the past, only few research activities addressed the acquisition and provision of product use feedback information for the improvement of future product generations [2–4]. Until now, however, the support of specific design decisions for the improvement of standard products has been considered only scarcely. Due to the lack of appropriate methods and tools, the improvement of existing standard products in current industrial practice is chiefly made individually, subjectively, and not systematically. This often leads to suboptimal or over-engineered products. The increased availability of digital information about current product generations, as well as progresses in decision theory offer new opportunities to optimize the improvement process of standard products. Therefore the solution proposed in this paper adapts and extends existing general design theories and methods and offers a framework for the product improvement process by exploiting available product use information. 2. Methodical approach 2.1. Product focus The proposed solution considers standard mass-produced products with a long lifetime and a variety of applications and
* Corresponding author. E-mail addresses: [email protected], [email protected] (M. Abramovici). 0007-8506/$ – see front matter ß 2013 CIRP. http://dx.doi.org/10.1016/j.cirp.2013.03.076
operational environments. It was assumed that these products are improved periodically, and that they are equipped with embedded micro-sensors capturing use information (e.g. operational temperature and rotation speed) mainly for condition monitoring purposes. A further assumption was the availability of large sets of product use data for analysis tasks. 2.2. Data sources and data types The new decision support solution chiefly uses data about the use of the current product generation that can be provided by customers and service providers [5]. That information contains technical product sensor data (e.g. work load), operational environment data (e.g. temperature), operational data (e.g. use time), product service data (e.g. failures), or product user data (e.g. use of specific functions). That product use data can be acquired from condition monitoring or service databases, customer relationship management systems, or Internet portals. That data can be objective or subjective, structured (e.g. sensor data) or unstructured (e.g. texts). The proposed solution focuses on objective, structured data about the product use, the product operational environment, and service events. 2.3. Decision support framework The developed decision support framework for the improvement of standard products is described in Fig. 1. The drivers for product improvements are product failures, deviations in product performance compared to the expectations, new similarly better or cheaper competitive products, new emerging technologies which could substitute current ones, as well as new relevant laws and regulations which affect existing products. For these reasons new product improvement requirements must be defined. These requirements extend the existing product specification.
160
M. Abramovici, A. Lindner / CIRP Annals - Manufacturing Technology 62 (2013) 159–162
Fig. 1. Decision support framework for the improvement of standard mass-produced products.
The product improvement process should follow all or some of the same stages as within the process of new product development, including requirement analysis, functional, principle, and embodiment design. Compared to the development process of new products the product improvement process occurs within very strong boundaries and constraints. The main existing product specifications (requirement lists) as well as the basic functional, principle, and embodiment product structures are already set. The product developer can only modify, substitute, extend, or cancel a few items within these structures. These changes require a lot of difficult decisions. Some examples of such decisions arising during the product improvement process are: Will the new requirements for product improvement affect the existing product specifications? Does the new product specification affect the existing functional, principle or product structures? Is a cancelation, substitution, modification, or extension of existing items necessary? If so, what will be the consequences or the impact of each item change on the product functions, features, and performance? In our approach the development process of improved products is regarded as a sequence of iterative decision cycles, considering an uncertain product operational environment and many decision optimization criteria. In the last decades a variety of general decision theories and methods have been developed and implemented in different application areas [6]. The solution proposed in this paper adapts and extends the rational, prescriptive, multi-criteria decision theory under uncertainty, as well as several decision support methods to assist relevant operative decisions in the improvement of standard mass-produced products. 2.4. Reference decision making process Based on classical prescriptive decision theory the following reference process has been developed for each decision cycle in product improvement. A. Problem analysis Problem identification Problem diagnosis (cause detection) Definition of improvement objectives (decision criteria) B. Generation and selection of solution alternatives Generation of solution alternatives Evaluation of solution alternatives Selection of a solution alternative C. Implementation of the selected solution alternative Product design change Product improvement prototyping, realization and evaluation Improved product operation, monitoring, and evaluation
The focus of the developed decision support framework covers the first two decision stages (A and B). The following section describes the specific methods supporting the different tasks of these two decision making stages. A more detailed description based on a validation sample will be made in Section 5.
3. Decision support methods 3.1. Problem analysis methods A problem in product use occurs if the product or modules of a product fails, or if the deviation of the product performance compared to its requirements exceeds the expected values. For problem identification classical statistical analysis methods have to be applied, e.g. investigating product use data or service data distribution (e.g. failure frequency, maintenance cycles, and workload). The second step in the decision process is problem diagnosis, namely the detection of the reasons which caused the problem. For example, problem causes can be an unfavorable product operational environment (e.g. an exceeded operational temperature) or a higher product workload than originally expected. These parameters cannot be changed by the product developer. In contrast to these operational and environmental use profiles, the product designer can optimize other design parameters like product type, material, dimensions, or shapes in order to improve the product. Problem diagnosis tasks include the identification of any mentioned unchangeable parameters as well as that of changeable product features, which cause the identified problems. For the problem diagnosis fault tree diagrams and/or ‘‘What-If’’ [7] analyses can be used [6]. The analysis of past product use data can show the probabilistic value distribution of the product operation environment. The influence of these distributions on problem events (e.g. on a product failure) can be calculated by a fault tree analysis based on Bayesian networks (BN). Bayesian Networks are oriented graphs, modeling events (e.g. product failures) and related influence factors modeled as nodes and their dependencies as directed edges. The probability distribution of different value intervals of each node (influence factor) is described by a conditional probability table (CPT). Inference algorithms based on these BN calculate the probable impact of the influence factors on other factors or on event nodes (e.g. probability of a failure). The use of traditional BN for problem diagnosis of standard products has been already used and described in a previous paper by the same authors [3]. This application considered unchangeable operation environment parameters only. In order to include an investigation of the impact of changeable product features on problem occurrence, object-oriented Bayesian networks (OOBN) have been chosen [8]. These OOBN and their related inference algorithms are similar to the classical BN but also allow the additional consideration of changeable product parameters (e.g. material and friction) and calculate their probable influence on problem occurrence (Fig. 2).
M. Abramovici, A. Lindner / CIRP Annals - Manufacturing Technology 62 (2013) 159–162
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traditional decision making methods (e.g. pairwise comparison) have been implemented. 5. Methods and tool validation
Fig. 2. Comparison of the used BN types.
In contrast to traditional BN the used OOBN cannot be learnt from product use data. The rough layout of the OOBN is given by the BN (cf. Section 2.2), but nodes describing product features must be added by the product developer. The CPTs of those additional nodes are assigned by the designer, based on service data not regarded in the initial BN. The third step of the problem analysis stage is the definition of improvement objectives, resulting from the extension of the initial product specification by the improvement requirements (e.g. longer module lifetime, shorter maintenance cycles). As the defined improvement objectives are not of equal importance, the product developers have to assign weights to each requirement/objective item. The new product specification is the main source for defining decision criteria for the evaluation of alternatives.
The use of the developed approach, methods, and tool prototypes have been validated by decision processes for the improvement of centrifugal pumps (Fig. 3). These products fulfill all the assumptions mentioned in Section 2.1. The functions, principles, structure, and components of centrifugal pumps, their operational environment, as well as the monitored product use data have been described by the authors in [5]. The monitored data of a large number of centrifugal pumps showed deviations of the pump’s behavior compared to the expectations defined in the existing product specification. That was the reason for initiating a product improvement project. The improvement process has been carried out by using the approach, methods, and software prototype described in Sections 3 and 4.
3.2. Methods for the generation and selection of alternatives The generation of alternative solutions to solve existing problems is a creative process that can only be supported by brainstorming or innovation techniques (e.g. TRIZ) [1]. For that product developers can use their experiences and different, relevant, available solution catalogs. In a next step the created alternative solutions have to be evaluated against the decision optimization criteria. For this evaluation classical quantitative methods (e.g. cost benefit analysis), qualitative methods (e.g. strength, weaknesses, opportunities, and threats analysis), or combined methods (e.g. analytic hierarchy process or pairwise comparison) [6] can be used. The result of using these general methods is a ranking of the considered alternative solutions, which can be the basis for a preselection of alternatives. In order to make a final decision, an assessment of the impact of the preselected alternatives on the original detected problems can be performed. For this consequence analysis the OOBN and the related inference algorithms described in Section 3.1 can be used for a ‘‘What if’’ analysis. Within this analysis the product developers can change several values of the CPT for some of the changeable influence nodes and check the probable influence on events (e.g. failure). Upon successful checking of all of the influence nodes a final selection of a product improvement solution alternative can be made. 4. Software prototype implementation The proposed decision making process and methods have been implemented in a software prototype. This prototype uses an open source data warehouse system (PENTAHO) for the acquisition, storage, and management of all product use information, as well as an open source knowledge representation and inference environment (WEKA). The knowledge-oriented software allows the representation of the specific object-oriented Bayesian network (OOBN) models and supports impact or ‘‘What If’’ analyses. The backbone of the data warehouse data model is the product type structure (Bill of Material), which is imported from the Product Data Management System Windchill (by PTC) and entered use data collected from all of the monitored product instances. In addition some open-source software modules supporting
Fig. 3. Centrifugal pumps as product type validation sample.
5.1. Problem analysis An analysis of the statistical distribution of component failures and deviations of the expected pump performance parameters led to the identification of critical parts. In the considered sample one of the pump bearings failed and had to be maintained and changed more often than expected. Hence, this pump bearing had to be improved. For the problem diagnosis an OOBN analysis as described in Section 3.1 was applied. First a BN has been learnt using aggregated data captured from several pump instance working space sensors (e.g. temperature), as well as operational conditions (e.g. workload, rotation speed). As these parameters cannot be influenced by the product developer, it is possible to assume that the same retrospective distribution will occur during the future use of an improved product. Then nodes describing product features and their CPTs were added by the product developer. Hence the possible influencing factors on the bearing failure were identified and displayed in a fault tree (OOBN). A change of the product design parameters (e.g. material, limitation of the rotation speed, or a change of the lubricant parameters) and the use of the OOBN inference algorithms showed that a change of the bearing features or type under the same product operation conditions can significantly reduce the bearing failure probability. In the following the requirements for the bearing improvement were defined. Considering the main identified problems the main improvement objective was to expand the lifetime of the product by extending service intervals and the mean time between failures and by maintaining or improving all of the characteristics described in the initial product specification. The new product specification has been generated from the improvement
M. Abramovici, A. Lindner / CIRP Annals - Manufacturing Technology 62 (2013) 159–162
162
requirements/objectives and from the initial product specification. Then, improvement objectives have been weighted according to their importance. 5.2. Generation and selection of alternatives The problem identification and diagnosis indicated a necessity to change the features of one pump bearing. The first considered alternatives were the change of the bearing dimensions or characteristics. Due to dimensional constraints the only option was to change the bearing features by substituting the existing bearing (ball bearing) with another bearing type with the same dimensions. The considered alternatives were a groove, a cylinder, a roller, or a journal bearing. Their characteristics have been chosen from standard product catalogs. The preselection of alternative solutions was made by using the defined weighted improvement objective list and a pairwise comparison of the alternative solutions with the existing solution (Table 1). The weighted sum of the assessed rating points led to a ranking of the chosen alternatives. According to the ranking, the alternative with the highest total score, the roller bearing was chosen for further evaluation. Table 1 Exemplary use of the pairwise comparison method for ranking decision alternatives. Improvement objectives (decision criteria)
Weight
Groove bearing
Sum of weighted rates (%) Ranking:
populations of product use data sets have been created by random generators. Nevertheless the rapid proliferation of low-cost product-embedded micro-sensors as well as the increasing control of product operation by the product providers within emerging product-service offers dramatically enhance the availability of product use data, which can be exploited for the improvement of future product generations. 6. Conclusion and outlook
Cylinder bearing
Roller bearing
Journal bearing
SR
WR
SR
WR
SR
WR
SR
WR
1 3
8 54
2 3
16 54
4 3
32 54
2 4
16 72
4
24
4
24
4
24
5
30
.. .
.. .
.. .
.. .
.. .
.. .
.. .
.. .
9
4
36
4
36
5
45
3
27
14
5
70
3
42
3
42
2
28
2 .. .
5 .. .
10 .. .
5 .. .
10 .. .
4 .. .
8 .. .
1 .. .
2 .. .
100
326
314
426
264
2
3
1
4
Existing requirements Reliability (>90%) 8 Mean time to 18 repair (<1 h) 6 Installation costs (<200s) .. .. . . New requirements Long lifetime (>10.000 h) Mean time between failures (>30d) Disposal cost (<50s) .. .
Decision alternatives
Fig. 4. Exemplary use of an OOBN for the failure prediction of a ‘roller bearing’ within a centrifugal pump.
Fulfillment compared to the existing solution: 1–2: worse; 3: equal: 4–5: better. SR: simple rates; WR: weighted rates.
For a verification of the chosen alternative solution a prediction of the behavior of this solution can be made by adapting the OOBN generated in the problem diagnosis stage. The value distribution of the operation environment parameters in this OOBN cannot be influenced by the product developer. The product feature node distribution values, however, can be changed to calculate the probability of a bearing failure when using a different bearing type (here: roller bearing). In the above example the product developer can use the knowledge-based inference software by carrying out a ‘‘What If’’ analysis (Fig. 4). According to that analysis the assessment of the new product features of the roller bearing leads to a very low failure probability. Hence this solution has been chosen and will be implemented. The validation of the developed methods and tools confirms their feasibility and shows their potential of enhancing decision processes for the improvement of standard products. Unfortunately a major problem when using these methods is the still low availability of product use data. For validation purposes large
The presented solution has a high potential to enhance decision processes for the improvement of standard mass-produced products. The proposed methodology extends and adapts general prescriptive decision theory and decision methods, and exploits sensor and operational data from the use of previous product generations. Future research work will consider additional data sources (e.g. customer, service, user databases), further data types (e.g. unstructured or semi-structured data), and will focus on the development of further new analysis, diagnosis, and decision making methods, as well as on the improvement of visualization techniques. Acknowledgements We express our sincere thanks to the German Research Foundation (DFG) for financing this research within the project ‘‘Product Lifecycle Management Extension through KnowledgeBased Product Use Information Feedback into Product Development’’.
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