Pergamon Computers ind. Engng Vol. 35, Nos 1-2, pp. 193-196, 1998

© 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain

P I I : S0360-8352(98)00059-X 0360-8352198 $19.00 + 0.00

MONITORING, DIAGNOSIS AND CONTROL OF INDUSTRIAL PROCESSES

S.M. Alexander and T.B. Gor

Department of Industrial Engineering, University of Louisville Louisville, KY. 40292

We present a framework for monitoring, diagnosis, and control of industrial processes. This framework utilizes the multiresolution analysis capability of wavelet theory. Wavelet coefficient patterns at different scales, under a variety of process conditions, are noted to form process f'mgerprints, these fingerprints yield process fault diagnosis. This knowledge facilitates efficient control. © 1998 Elsevier Science Ltd. All rights reserved.

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We present our research on monitoring, diagnosis and control of industrial processes. There are three main reasons for this research. First, the assumptions underlying the widely used statistical monitoring techniques are rarely satisfied in practice. Second, even when monitoring schemes are successfully applied they provide very little, if any, information on the cause of process instability. And third, recent literature has advocated the use of Engineering Process Control (EPC), widely used in the chemical process industry, for regulating, discrete part manufacturing[Box and Luceno, 1997]. However, it is not always efficient to regulate an unstable process. Thus, some researchers have suggested integrating SPC with EPC, however, the autocorrelated structure of regulated output makes monitoring difficult. We present a framework for monitoring and control that overcomes these shortcomings. Our framework utilizes the multiresolution analysis capability of wavelet theory to gain additional insight from process characteristic measurements. A unique feature of this framework is that it integrates diagnosis with monitoring and control, and it is not constrained by the underlying distributions of the process characteristics being monitored. In the next section we define SPC and EPC. Following that we illustrate our framework and test its feasibility for monitoring process shifts and forming a diagnosis.

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The current modes of monitoring and control used in industry include statistical process control (SPC) and engineering process control (EPC). SPC is mainly used in the discrete parts manufacturing environment. It is a statistical monitoring tool. When instability is indicated on an SPC chart, operator intervention is normally required to search for, and remove the root cause of instability. Since SPC charts offer no guidance to the operator in determining the root cause, the operator is often frustrated in this search.

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194 23rd International Conference on Computers and Industrial Engineering

EPC is commonly found in continuous process industries. Here feedforward and feedback control systems, which are usually automated, are used to maintain a process on target, by manipulating control variables. The recent research literature has advocated combining the methods of SPC and EPC. Since it is not always effective to take corrective action only when the indicators of instability are statistically significant as is the case with SPC, nor is it efficient to only take compensatory or regulatory control actions on a process and ignore the root cause of a process upset, as in EPC.

Framework for Monitoring. Diagnosis and Control.

Our framework for process monitoring, diagnosis and control is illustrated on Figure 1. Our premise is that the choice between process regulation and correction requires information on the cause of process instability, hence, we attempt to integrate diagnosis with monitoring and control. In our framework, process characteristic measurements axe passed through a generic EWMA filter, since filtered data have been found to be more efficient for the detection of process upsets [Box, 1997]. The filtered process data are then directed through a multiresolution analyzer based on wavelet theory. We evaluate the utilization of wavelet theory for both detecting process instability, and the localization of process faults. With this partial diagnosis we are able to select the most appropriate control action.

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Fig. 1. FRAMEWORK FOR PROCESS MONITORING, DIAGNOSIS & CONTROL

Wavelet theory enables one to look at observations of process characteristic values via a "mathematical microscope" in multiple resolutions, hence, the term mulfiresolution analysis. The mathematical form of wavelet transforms can be found in [Young, 1993] Through multiresolution analysis subtle changes in form, amplitude and frequency characteristics of process observations can be detected. We hypothesize, based on preliminary tests [See Gor and Alexander 1997], that detecting these changes not only

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allows us to detect process instability, it allows us to define a diagnosis, i.e. a cause for the instability. In the next section we illustrate the detection of a variety of process shifts by monitoring changes in these wavelet transforms.

Muitiresolution Analysis applied to the Detection of Process Shifts

We first simulate process common process upsets, in the form of spikes, steps, trends and cycles on simulated characteristic data and develop approaches to detecting these by deriving statistics and corresponding detection thresholds for the transforms. Our approach was to select the resolution level at which the particular process shift was evident visually, and to study the distributions of the transforms at this scale with and without the shift. Figure 2, for example, indicates the distributions of transforms at resolution level 1, where sporadic spike shifts are usually visible. The normal distribution of the transform coefficients allow us to easily specify the threshold coefficients at + .4 and the simulated spike is readily evident. Similarly we define a threshold for a step shifts, trends and cycles. Step shifts for instance are detected at approximation(denoised) level (a2) as shown in figure 3. In order to sensitize our system to very small changes we evaluate the use of Cuscore statistics for trends and cycles. We also plan to evaluate the use of Likliehood Ratio tests for this purpose.

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196 23rd International Conference on Computers and Industrial Engineering

Diagnosis via Wavelet Transforms

In the previous section we have indicated the feasibility of detecting a variety of process shifts using multiresolution analysis. We have seen that multiresolution analysis has the capability of detecting subtle changes in the form, magnitude, and frequency of process characteristic measurements. We hypothesize that these changes can be mapped to a diagnosis. This diagnostic information can be used for control purposes. In order to test our hypothesis we apply our analysis to a some industrial problems, these are reported in Gor and Alexander, '97. These applications suggest that multi-resolution analysis does provide discriminatory evidence for various faults.

Our present procedure, therefore, has been to use multiresolution analysis and detection thresholds to monitor changes in process characteristic values and map these changes to a diagnosis, via a decision table. We plan to evaluate the use of neural nets to for direct translation of these changes to a diagnosis.

Conclusions and Extensions

With manufacturing becoming more automated, many online sensors for process monitoring and control have been developed. However the methods used for control have not adapted to this change, nor have these methods used the information available effectively. Our proposed system gleans the underlying form, amplitude and frequency

information of process data for monitoring, diagnosis and control, and unlike pure statistical approaches, the efficiency of the monitoring system does not depend on the distribution or autocorrelation of the process characteristic being monitored. In this paper we have described portions of a framework for monitoring, diagnosis and control. In our research, so far, we have confined our analysis to the observation of only a single process characteristic. We anticipate that when multiple process parameters are monitored simultaneously, the diagnostic module will evaluate corroborative and complementary evidence to reach a diagnosis.

References

1. Box, G.E.P. and Luceno, A. (1997). "Statistical Control by Monitoring and Feedback Adjustment" John Wiley and Sons, New York.

2. Gor, T.B. and Alexander, S.M. (1997). "Process Monitoring and Diagnosis Through the Analysis of Non-Stationary Signals", Proceedings, Industrial Engineering Research Conference, pp. 456-460.

3. Young, R.K. (1993) "Wavelet Theory and its Applications", Kluwer Academic Publishers.