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Journal ArticleDOI

A self-validating control system based approach to plant fault detection and diagnosis

15 Mar 2001-Computers & Chemical Engineering (Elsevier Science)-Vol. 25, Iss: 2, pp 337-358
TL;DR: In this paper, an approach is proposed in which fault detection and diagnosis (FDD) tasks are distributed to separate FDD modules associated with each control system located throughout a plant.
About: This article is published in Computers & Chemical Engineering.The article was published on 2001-03-15 and is currently open access. It has received 31 citations till now. The article focuses on the topics: Control reconfiguration & Fault detection and isolation.

Summary (3 min read)

1. INTRODUCTION

  • For the sake of both economy and safety, online process monitoring, fault detection and fault diagnosis have received significant attention in recent years.
  • Since the hardware modules associated with these control loops are distributed throughout the plant, it seems sensible to distribute associated detection & diagnosis tasks in a similar manner.
  • Considerable research and debate are required before practicable implementations evolve.
  • Being steady state based, the concept is independent of any time delays in the plant.
  • The focus here is on the two most relevant aspects: on distributing diagnostic tasks to control systems and on those non-distributed methods that might be viewed as having some similarities with the approach described here.

2. REPRESENTATIONAL ISSUES FOR SEVACS KNOWLEDGE GENERATION

  • This section examines various ways that cause-effect knowledge can be represented to facilitate its generation.
  • The first step is to introduce nomenclature relating to block diagram representations of two standard control systems (Section 2.1).
  • These block diagrams are then analysed in Section 2.2 to produce equations that can generate cause-effect knowledge.

2.1 Nomenclature

  • The various variables used are defined before going any further.
  • Parameter Kc is the proportional gain of the controller and parameters Kv, Kp and Kd are respectively the valve, process and process disturbance steady state gains.
  • Note that this structure represents only one form of PID control.

2.2 Generation Of SEVACS Inter-Node Relationships

  • This sub-section examines how faults and process disturbances can affect individual control systems, the results are then used to construct SDG representations in the next sub-section.
  • A similar approach can be taken for the cascade case.
  • In both cases, and for both stable and unstable processes, deviations in [dv] or [dp] will have the same effect on [x].
  • The directions of the deviations in the various observations can provide additional information with which to infer the ‘direction’ of the various fault hypotheses e.g. “fails-high” or “fails-low”.

2.3 Representing Control Systems By SDGs

  • In Figure 6, the circles around nodes D and E indicate that these nodes are measured; hence node F, which is not circled, is unmeasured.
  • Figure 7(A) shows an SDG representation of a typical single loop control system, in which C, V, X and M represent the controller output, the valve opening, the controlled variable and the sensor measurement respectively; θr, dv, dp, dm represent deviations in setpoint, valve bias, process disturbance and sensor bias respectively.
  • Individual elements should still be treated separately when performing fault diagnosis.
  • This super-node can be analysed 14 using control system related cause-effect knowledge that will be discussed in the next section.
  • It is worth pointing out that, as has been discussed, for stability the sign product of any of the control loops in the above SDGs must be ‘−’.

3. SEVACS CAUSE-EFFECT KNOWLEDGE

  • Results from the previous section can now be applied to generate tables of causeeffect knowledge, which can be downloaded to the SEVACS.
  • The contents of the tables differ depending on whether or not the process has a Type Number of zero.
  • Equations (8) — (14) were referred to extensively when deriving this knowledge.
  • Tables 2 and 3 describe the various effects that individual faults would have on the observations available for single loop and cascade loop control systems respectively.
  • These faults would be addressed by using other approaches.

A sensor bias in a single loop control system or in the outer loop of a cascade control

  • If the sensor biases, the controller will take action to compensate for this with 15 the net effect that there will be a deviation in the controller output and the sensor measurement will return to its normal value.
  • Both the and the outer loop controllers will attempt to compensate with the net effect that the sensor deviation observed (Ds) will have the same direction as the sensor bias.
  • The decision table in the Figure 10 summarises this.
  • The direction of the exogenous/ancestor fault or disturbance can then be determined by looking at the following: R*: the relation between a sensor measurement and a controller output; Rex: the relation between an exogenous variable and a sensor measurement; D*: the steady state deviation in a controller output.
  • The direction of the valve bias can then be determined by looking at the following factors : Rcv: the relation between the controller output and the valve opening; Dc: the steady state deviation in the controller output.

4.1 Control Systems with Uni-directional Interactions

  • A simple set of rules can be derived for those control systems with uni-directional interactions that have the fairly general feature shown in Figure 13.
  • If S1 pertains to a Type Number 0 controlled process and its control loop deviates (any element in the control loop deviates), then, initially, the fault candidate will be {S1-sensor-bias, E1, E2, valve-bias in the S1 control loop}.
  • There are now two possibilities: 17 S2 is affected: because E1 is the common ancestor of S1 and S2 and according to the fault isolation principle, the fault candidate set shrinks to {S1-sensor-bias, E1}; if the direction of the deviation of S2 contradicts that expected from the S1-sensorbias, {E1} is the only fault route.
  • If there is no more information about E2, then these two possibilities can not be separated.
  • Otherwise, if E2’s descendants deviate, E2 will be the only fault route.

4.2 Control Systems with Bi-directional Interactions

  • Here the controlled variables S1 and S2 affect each other ; either can pertain to a single loop (s.l.) control system or to the inner loop (i.l.) or to the outer loop (o.l.) of a cascade control system.
  • There must be at least one common ancestor, which is the fault.
  • RS1S22 and RS22S1 represent the relations (or interactions) between the two controlled variables S1 and S22. 4.2.3 Type C Interaction: Inner Loops of Both Cascade Control Systems Interact A Type C interaction is shown in Figure 17: one inner loop controlled variable S12 interacts with the other inner loop controlled variable S22.
  • First consider the situation in which the controlled processes here are not capacitive.

5. AN ALTERNATIVE FAULT ISOLATION METHOD FOR INTERACTING CONTROL SYSTEMS

  • The procedures described in the last section require different knowledge or rules for different processes.
  • Now consider the case where additional knowledge is available e.g. in the form of Figure 23: note first that a common disturbance to F, L and T doesn’t exist and hence L-sensor-bias-high is the only fault that can be diagnosed.
  • Both the outer and the inner loop controllers of the temperature control system will deviate, as will CA.
  • If Figure 23 is known, the common disturbance can then be replaced with K or K0, and the high-CA can be replaced with high-CA0.

7. CONCLUSIONS

  • A self-validating control system based approach to plant fault detection and diagnosis has been proposed that enables the distribution of these tasks throughout a plant.
  • The approach itself is targeted on control systems that inherently eliminate steady state error; it is modular, steady state based, requires very little process specific information and should therefore be attractive to control system’s implementers who seek economies of scale.
  • Blatantly obvious faults like sticking valves are not accommodated, but these can easily be detected and isolated using a simple rule-base, which can also be distributed to the FDD modules.
  • The approach would not be able to detect the presence of a sensor bias if it existed at the time the plant was started up.
  • The authors suspicions are that the difficulty, once again, would be more to do with the existence and identification of some form of quasi- steady state, than to revising the approach to accommodate these ‘special cases’.

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Citations
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Journal ArticleDOI
TL;DR: Two case studies are presented to illustrate SDG-based analysis of process flowsheets containing many units and control loops and it is shown that digraph-based steady-state analysis results in good diagnostic resolution.

135 citations

Journal ArticleDOI
TL;DR: In this paper, the authors focus on the systematic development of graph models and the conceptual relationship between the analysis of graph model and the underlying mathematical description and the analysis procedures for the graph model.
Abstract: In the recent past, graph-based approaches have been proposed by various researchers for safety analysis and fault diagnosis of chemical process systems. Though these approaches have shown promise, there are a number of important issues that have not been adequately addressed in the literature. The issue of systematic development of graph representations for chemical processes has not been addressed in the literature. This is an important issue because the development of digraphs is error-prone and time-consuming. Further, little attention has been paid toward understanding the conceptual relationship between the underlying mathematical description and the analysis procedures for the graph model. Also, the utility of these graph-based approaches at a flowsheet level has not been studied. With these issues in perspective, in this first part of the two-part paper, we focus on the systematic development of graph models and the conceptual relationship between the analysis of graph models and the underlying ma...

124 citations

Journal ArticleDOI
TL;DR: A combined signed directed graph (SDG) and qualitative trend analysis (QTA) framework for incipient fault diagnosis that combines the completeness property of SDG with the high diagnostic resolution property of QTA.
Abstract: In this article a combined signed directed graph (SDG) and qualitative trend analysis (QTA) framework for incipient fault diagnosis has been proposed. The SDG is the first level in this framework and provides a possible candidate set of faults based on the incipient response of the process. The search for the actual fault is performed based on a QTA (level 2), which uses the temporal evolution of the sensors for further resolution. Thus, this framework combines the completeness property of SDG with the high diagnostic resolution property of QTA. Methods to address the problem of incorrect diagnosis arising due to incorrect measurement of initial response have also been presented. The proposed approach is tested on the Tennessee Eastman (TE) case study. Correct fault diagnosis is performed in all possible single fault scenarios. It is shown that this framework provides fast, reliable and accurate incipient fault diagnosis.

93 citations


Cites background from "A self-validating control system ba..."

  • ...This is due to loss of information while going from quantitative to qualitative domain (Chang and Yu, 1990; Chen and Howell, 2001; Iri et al., 1979; Oyeleye and Kramer, 1988; Tarifa and Scenna, 2003; Tsuge et al., 1985; Wang et al., 2002; Wilcox and Himmelblau, 1994)....

    [...]

Journal ArticleDOI
TL;DR: In this paper, a unified SDG model for control loops is discussed, in which both disturbances (sensor bias, etc.) as well as structural faults can be easily modeled under steady-state conditions.

83 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a signed digraph (SDG) model for control loops and discuss a framework for application of graph-based approaches at a flowsheet level.
Abstract: The objectives of this part of the two part paper are (i) development of signed digraph (SDG) models for control loops and (ii) discussion of a framework for application of graph-based approaches at a flowsheet level. Further, two case studies are used to explain the methods developed in part 11 (Ind. Eng. Chem. Res. 2003, 42, in press) and this paper. The first case study (continuous stirred tank reactor case study) explains the basic concepts of the generate and test method for SDG analysis, generation of redundant equations using algebraic manipulation, and analysis of systems with a single control loop. Case study 2 (flash vaporizer case study) deals with different methods of generating redundant equations and the analysis of systems with multiple interacting control loops.

78 citations

References
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Dissertation
01 Jan 2000
TL;DR: This thesis envisages the situation where the detection and diagnosis of faults and disturbances would be distributed to separate modules, each associated with the individual control systems located throughout a plant, and seeks to address those plants whose control systems inherently eliminate steady state error.
Abstract: For the sake of both economy and safety, the ability to diagnose a fault or disturbance is of great interest for an operator/engineer in process industries. To be practicable an on-line system with this capability must contain a suite of methods because no single method is likely to diagnose all possible faults. This thesis aims to contribute one novel component to this suite. This thesis envisages the situation where the detection and diagnosis of faults and disturbances would be distributed to separate modules, each associated with the individual control systems located throughout a plant. In particular the thesis addresses those plants whose control systems inherently eliminate steady state error. Thus it seeks to address the large proportion of process plants that have proportional plus integral action as standard. By reasoning about changes in steady state an approach is proposed that requires very little process specific information and therefore should be attractive to control systems implementers who seek economies of scale. Because the approach can be implemented as modules that are largely based on standard control systems, the implementation can be configured and commissioned using various generic programmes and hence has the potential to be commercialised. The approach is applicable to virtually all types of process plant, whether they are open loop stable or not, have a type number of zero or not and so on. It is founded on the application of both signed directed graph (SDG) and control systems theory to single and cascade control systems with integral action. This results in the derivation of cause-effect knowledge and fault isolation procedures that take into account factors like interactions between control systems, and the availability of non-control-loop- based measurements. Following on from a survey of the more relevant methods published in the literature, a theoretical analysis is carried out of what happens to control systems when they are subjected to various faults and disturbances. The main purpose is to derive equations to describe how control systems respond in the steady state to these occurrences. Although providing a foundation, these equations are unlikely to be suitable for direct use and a cause-effect analysis of the faults/disturbances involving signed-directed- graph (SDG) representation is then pursued. This leads to a search and test strategy for fault isolation involving interacting control systems, minimal knowledge acquisition and knowledge evolution. Since the approach is based on steady state deviations, a steady state change detection algorithm is proposed. The approach is tested by applying it to a continuous stirred tank reactor (CSTR) and to the Tennessee Eastman (T-E) process benchmark. Some recommendations are made for integrating the approach into a commercial software tool. In principle, the approach can form the basis for the diagnosis of faults/disturbances in both control systems and in the process itself. One of the key features is that the approach can work at different levels of detail. Diagnosis is based on knowledge of the signs of steady state interactions (gains) between individual control loops, non- control-system-related measurements and on the steady state effects of disturbances. Both faults and disturbances (e.g. a load change) can be diagnosed, although diagnostic detail, i.e. degree of isolation, is clearly dependent on the measurements and knowledge that is available. The concept of a distributed, control system based approach to the diagnosis of faults and disturbances, its development and application to various processes are all original, as are the integration aspects.

3 citations


"A self-validating control system ba..." refers background or methods in this paper

  • ...Referring to Figure 7(B), Table 4 has been derived by modifying and subsequently analysing Equation (12) for each controller: just simply replacing its [dp] term with a compound disturbance term that consists of the effect of the sensor bias in the other control system (Chen, 2000)....

    [...]

  • ...Details of step test procedures can be found in Chen (2000)....

    [...]

  • ...Only the main ideas are given here, a more comprehensive explanation can be found in Chen (2000)....

    [...]

  • ...Referring to Figure 7(B), Table 4 has been derived by modifying and subsequently analysing Equation (12) for each controller: just simply replacing its [dp] term with a compound disturbance term that consists of the effect of the sensor bias in the other control system (Chen, 2000)....

    [...]

  • ...Details of step test procedures can be found in Chen (2000). A simple SDG representation can be constructed, which is based on the above information....

    [...]

Journal ArticleDOI
TL;DR: In this article, the results of applying a steady state, distributed approach are described Normally more than one distributed module is found to respond to any given fault by applying a search & test strategy a Supervisor is able to integrate the various conclusions to isolate the fault or at least its locality.

2 citations


Additional excerpts

  • ...29 Although beyond the scope of this paper, its applicability has been demonstrated on the 24-loop Kodak Eastman benchmark (Chen & Howell, 2000)....

    [...]

Journal Article
TL;DR: In this article, the authors developed procedures for online steady state identification and for data reconciliation and error detection. The procedures can use the same set of easily calculated statistics and can be used to identify the steady state and process variable values due to instrument noise.
Abstract: One problem facing the process industries is process monitoring in the presence of random and biased instrumentation errors. One method is to reconcile the data to close a constraining steady-state model before calculating indices such as heat transfer coefficients. Another problem is identifying the steady-state and process variable values due to instrument noise. Procedures have been developed for online steady-state identification and for data reconciliation and error detection. Both procedures can use the same set of easily calculated statistics. The procedures are illustrated with a simple example.

1 citations


"A self-validating control system ba..." refers background in this paper

  • ...Albers (1997) has discussed the application of steady state identifiers to data reconciliation and error detection, whilst workers on real-time optimisers (see for instance, Pierucci et al., 1996) and PCA (see for instance, Vedam and Venkatasubramanian, 1999) do not comment on the issue....

    [...]

  • ...Albers (1997) has discussed the application of steady state identifiers to data reconciliation and error detection, whilst workers on real-time optimisers (see for instance, Pierucci et al....

    [...]