Intelligent control of multivariate signal analysis

Intelligent control of multivariate signal analysis O. A u m a l a Tampere University of Technology, Measurement Technology, PO Box 527, SF-33101 Tampere, Finland Multivariate signal analysis is a tool which can be used to study the behaviour of complicated technical systems in amplitude, time and frequency domains. Tools ani:l means to do meaningful analysis using a priori knowledge, and to master the large amount of information generated, are presented. The usefulness of the analysis method in explaining the transfer paths of various phenomena is illustrated by a case application. Keywords: Multivariatesignalanalysis,autoregressivemodelling,model-basedreasoning,transferpath analysis Introduction

The signal given by the measuring equipment is generally the primary source of experimental information. Signal analysis is used to study this information and disp!ay it in concise, useful form. Most often analysis will be done in amplitude domain, but also analysis of each signal in time or frequency domain is often used (Bendat and Piersol, 1986; Priestley, 1981). Multivariate signal analysis has proved to be very powerful when analysing complicated systems with many feedback paths (Aumala, 1988; Eklund etal, 1987; Markkanen, 1988; Rantala and Kokkonen, 1986). It has not been very popular, however. The reason is rather obvious: the amount of information is large, the calculation power needed is considerable, and the amount of results obtained may be overwhelming. Development in this field is highly needed, but problems in connecting heavy calculating with the knowledge-based reasoning of today is a complicated task. In Tampere university of technology we have had a research project (Eklund et al, 1989) and results are available in suitable form for industrial use (Saarela, et al, 1989) as well as a researcher's method-development version (Ihalainen, undated). As far as the author knows there seems to be certain use of the multivariate signal analysis in laboratory research, but published developments are few.

The classical method used in multivariate signal analysis is the Fourier method (Bendat and Piersol, 1986). The ambiguity and considerable errors for systems containing feedbacks have been one of the reasons why this method has not become more universally applied in practical analysis. Nevertheless, in scientific texts the Fourier method is the 'base' method. In more recent books (eg, Priestley, 1981) several other methods are introduced, and are available for signal analysis. The autoregressive method has sometimes been characterised as 'popular'. The main advantages for the autoregressive method are that this method • is causal (no output before input, a very common feature in practical systems) • is parametric (the amount of parameters may however be considerably bigger than that for a control system model) • is well suited for computers • is suitable for systems containing feedback paths • may analyse directions of transfer. The signal model behind the autoregressive analysis may be written as a matrix equation set: M

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The analysis may be done for various purposes such as • to check the state of the process (as a combination of several variables) • to study the dynamic properties (the dynamic model such as the state space model) of the system • to study the interactions between variables • to study cause-effect relationships using contribution analysis • to detect the source for disturbances using transfer path analysis • to monitor the working condition and need for overhaul of the equipment. Measurement Vol 9 No 1, Jan-Mar 1991

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ses. This kind of approach is applicable to the analysis of systems with feedback loops. Fig 1 shows a signal model consisting of three variables (Oguma and Tiirkcan, 1985). The identification of the MAR-model is based on covariance functions calculated from the measured signals. After estimating the multivariate signal model, various results may be computed and displayed. These results include spectral density functions of the signals and the noise sources, step response functions, transfer functions and noise contributions, which describe the contribution of each noise source to the spectrum of the signal under study.

Multivariate signal analysis is an excellent tool for an expert in signal and process analysis. Programs which apply these analysis methods are, however, quite complicated to use, because there are a lot of parameters and algorithms to choose from. The interpretation of the results produced by the program is not an easy task either. In addition to expertise in signal analysis, one must have a deep understanding of the process under study. Process models are needed from the very beginning of the analysis work. The first choice is where to put the measuring transducers for data recording, and the first symptoms together with a suitable process model are to be used in order to get a meaningful set of signals. Different abstractions are needed to describe the behaviour of the process. We have used structural and functional abstractions describing the physical connections of components in the process and their functional interactions, respectively. These abstracted models are represented as semantic networks in the knowledge base. The hierarchy consists of three levels: the global process level, which describes the process as a system composed of subsystems and their interactions; the subsystem level, which is composed of separate components; and the component level. Fig 2 shows the top level and an intermediate level description of a wet end of a paper machine.

4. The analysis system The analysis may be divided into several tasks. These tasks are shown in Fig 3 as a structure. Some tasks include mainly reasoning, others mainly computing. The configuration of the analysis systems has been built up using two computers, one of them a LISP-workstation and the other a VAX/VMS computer, Fig 4.

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Fig 4 Configuration of the analysis system One of the difficulties is the control of the multitude of tasks and operations. Therefore the user interface of the analysis system if very important. The interface is knowledge-oriented. It consists of menus, dialogue boxes and graphics. The dialogue box (Fig 5) is the most central tool for communication between the user and the program (J~irviniemi et al, 1989). The following aspects shall be considered (Markkanen, 1988): • selecting the measurement and analysis parameters • selecting the analysis algorithms and supervising the analysis • interpreting the output from the signal analysis program and expressing the results in a natural language • incorporating a description of the process in the system • incorporating knowledge about the diagnosis procedure (i e, how a diagnosis based on the MAR-model is done by a human expert) • interfacing the expert system module with the existing complicated analysis program.

4.1 Signal preprocessing There are several phases of procedure before the analysis itself. They may be given as an example list: • load the signal(s) • plot the signal • smooth spikes, and missing data points, and detect step-type errors or sensor saturation • linearise according to the characteristics of the sensor Measurement Vol 9 No 1, Jan-Mar 1991

• filter (most often low-pass filtering is needed) • change the sampling interval (this is called decimation) • remove the trend • normalise the data: remove the mean, divide with the standard deviation • test the normality (Khi-square test) • choose a suitable time interval to be analysed • go to the analysis In some cases the preprocessing work takes more time and effort than does the analysis itself. Intelligent routines are therefore needed. In process trouble-shooting work it seems that there are several useful routines available, but the preprocessing may not be fully automated. Smoothing spikes (Jokinen and Kaljunen, 1987), testing, and choosing a stationary time interval (Rantala, 1987) are examples of phases which can be done automatically in some cases. One should note that it may be necessary to go back to phases already done once. On the other hand, for well-known target processes it may be possible to do some preprocessing continuously and in a fully automatic way. In those cases also a full-automatic analysis-eg, for diagnostic purposes-is possible.

4.2 Diagnostic problem formulation The problem solving is started from the process context where a malfunction was detected. From the structural and functional models of the process, the system selects the signals that should be taken into the model (be measured in the next recording to be taken if not already available). The MAR-model is created with these signals, and interactions at the specified frequency range are checked in order to find out the transmission path of the malfunction. This is most often achieved by inspecting the noise contribution of the signal where the malfunction is known to be present. It is frequently appropriate to create new models. When the signal has been identified where the malfunc|()%11; - II;ll~t Ill;tllil)tll;lll

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of some types of industrial processes. An example of a paper machine analysis follows (Aumala, i988; Rantala and Kokkonen, 1986). This particular case is chosen to represent a very common instability problem for paper machines-basis weight variation in the machine direction. Despite the new and carefully designed paper mill stock system, the long term basis weight variation was higher than with the best similar paper machines. Since this type of disturbance usually is random variation and may be found in many points in the controlled process, conventional analysis methods do not easily lead to correct conclusions. The paper machine approach piping is a two pump and deaerator system. The simplified flow diagram is shown in Fig 8. Pressure, consistency, level, flow, and speed variation measurement locations are also shown in the diagram. The dry end basis weight gauge was used in single point position and recorded simultaneously with process variables. The first step in the analysis was to study machine direction (MD) basis weight profile of the paper. It could be seen from its spectrum that most of the intensity was

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4.4 Analysis documentation The user interface provides some tools for documentation. The graphs and other information can be collected to a special window by copying or moving from any of the analysis windows. The analyst may add text comments and then hardcopy the window to a standard report page. In routine analysis it is possible to do the document collection automatically.

4.5 Basic sequence of operation The operational modules of the diagnosis system are presented in Fig 7. This is an overall description of the sequence in which diagnosis is done, not a detailed flow diagram.

5. Case application The multivariate signal analysis programs have been applied for trouble-shooting and condition monitoring 26

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Fig 8 Paper machine stock system: (1) medium-consistency pulp; (2) drainage sump; (3) mixer pump; (4) centrifugal cleaner; (5) de-aerator; (6) feed pump; (7) rotary strainer; (8) headbox; (9) wire below 0.1 Hz. Total head pressure and pulp consistency variations had most energy at low frequencies, also. The system under study has many feedbacks and so the interfering frequencies may be found everywhere. Looking at the contribution analysis of the basis weight, Fig 9, variation at the frequency 0.015 Hz seems to be fully explained by consistency variation, but at other major areas the variation can be due to a combined contribution. The next part of the analysis was to find out the cause for the disturbances mentioned in the stock system. For this purpose a recording was made simultaneously measuring those process variables which are able to cause low consistency variation after the primary fan pump. Specially installed pressure and consistency transducers, as well as fixed instrumentation transmitters, were used. Further contribution analysis showed that the measured inputs explain the output well. The 0.015 Hz disturbance was primarily explained by pressure variation of the deaerator. Further analysis work was then necessary in order to study, in detail, why the mentioned disturbance existed. 6. D i s c u s s i o n

Multivariate signal analysis gives two essential challenges. One is the need to master vast amounts of information. Graphic representation together with a sound structuring of the work has proved to be very effective in this respect. The other challenge is control of the analysis work using a priori knowledge. Process models and recipes have given good experience; they seem to be a suitable means to solve this problem. Further research is needed to develop a straightforward way to program recipes. The mathematical foundations of multivariate signal analysis have already been known for a long time. After giving the possibilities to develop efficient applications, the analysis methods are coming into widespread use.

Metrological Assurance for Environmental Control, Helsinki, August. Bendat, J. S. and Piersol, A. G. 1986. Random data, analysis and measurement procedures (2nd ed). Wiley-Interscience, New York. Eklund, J., Ikonen, K., Leskinen, J. and Jnkinen, H. 1987. Multivariate signal analysis-an efficient tool for power plant analysis. IMEKO 6th TC7 Symposium, Budapest, Hungary. Eklund, J., lhalainen, H. and Kokkonen, O. 1989. A knowledge-based system for process diagnostics and trouble-shooting; Summary report for MOSIG Project. Tampere University of.Technology, Measurement Technology, Report No 44, Tampere. Ihalainen, H. Multivariate signal analysis in MATLAB. To be published by Tampere University of Technology. Jokinen, H. and Kaljunen, T. 1987. Extraction of power spectrum peaks. Technical Report No 42, Tampere University of Technology, Department of Electrical Engineering, Finland, (in Finnish). J/irviniemi, A., Jokinen, H. and Pohja, S. 1989. User interface in signal analysis expert system. Scand Conf on Artificial Intelligence, Tampere, Finland, June. Markkanen, H. 1988. Combining signal analysis and expert system techniques in power plant diagnostics. Acta IMEKO. Oguma, R. and Tiirkcan, E. 1985. Application of an improved multivariable noise analysis method to investigation of PWR noise; Signal transmission path analysis. Progress in Nuclear Energy. Vo115, pp 863874. Pergamon Press, Oxford. Priestley, M. B. 1981. Spectral analysis and time series. Vol 1 and 2. Academic Press, London, 890 pp. Rantala, S. 1987. A rule-based system to find out stationary data segments from a random process. IMEKO 6th TC7 Symposium, Budapest, Hungary. Rantala, S. and Kokkonen, O. 1986. Application of multivariate AR-modelling in analysing paper machine approach piping disturbances. 6th Int IFAC/IFIP/ IMEKO Conf on Instrumentation and Automation in the Paper, Rubber, Plastics, and Polymerization Industries. Akron, Ohio, USA, pp 50-54. Saarela, O., Markkanen, H., Mustonen, H. and Rantala, S. 1989. Detection of process disturbances using model-based reasoning. The 2nd Scand Conf on Artificial Intelligence, Tampere, June.

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