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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

AbstractAn 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. Intended specifically for those control systems that inherently eliminate steady state error, it is modular, steady state based, requires very little process specific information and therefore should be attractive to control systems implementers who seek economies of scale. The approach is applicable to virtually all types of process plant, whether they are open loop stable or not, have a type or class number of zero or not and so on. Based on qualitative reasoning, the approach is founded on the application of 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 sensors.

Topics: Control reconfiguration (62%), Fault detection and isolation (60%), Control system (55%), Fault model (55%), Stuck-at fault (55%)

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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1
A SELF-VALIDATING CONTROL SYSTEM BASED APPROACH
TO PLANT FAULT DETECTION AND DIAGNOSIS
Jun Chen
*
and John Howell
*
Keywords: fault diagnosis, fault isolation, distributed control, process control
*
Department of Mechanical Engineering, University of Glasgow, Glasgow G12
8QQ, UK

2
ABSTRACT
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. Intended specifically for those control systems that inherently
eliminate steady state error, it is modular, steady state based, requires very little
process specific information and therefore should be attractive to control systems
implementers who seek economies of scale. The approach is applicable to virtually
all types of process plant, whether they are open loop stable or not, have a type or
class number of zero or not and so on. Based on qualitative reasoning, the approach is
founded on the application of 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 sensors.

3
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. Becraft and
Guo et al. (1991) have surveyed a number of methods pertaining to fault diagnosis
and have pointed out that each method manages to capture or model some subset of
the features of diagnostic reasoning about operating conditions, and thus may be more
suitable than other techniques for a particular class of problem. Although a lot of
approaches have since been developed, still none can be viewed as having general
applicability. Heuristic methods are fast and do not require a plant model, but are
comparatively brittle because they cannot handle situations that are not explicitly
anticipated. Model-based techniques are less brittle, but pose other problems: most
industrial chemical processes are unique, it is expensive to build high-fidelity first-
principles models of these processes and very difficult to anticipate all the abnormal
situations that might arise; neural network based methods require considerable data
and long training times and might have difficulty in diagnosing novel faults.
The main motivation for the development of the method described here is to provide a
distributed scheme, because virtually all the other methods take a centralised view in
that design & analysis are normally carried out from the top, say from a plant
schematic, and FDD tasks are implemented centrally on something like a DCS
Supervisor. This appears to be contrary to current developments: to exploit economies
of scale, “economic pressures are dispersing machine intelligence away from
centralized computers toward distributed (Fieldbus) devices “ (Clarke, 1995). If one
looks at the measurements collected from a process plant, a large proportion relate to
the control loops, the rest are largely collected to ensure that operation is within
allowable constraints. 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. The role of each of these detection &
diagnosis modules would not be confined to the validation of the performance of the
closed loops, each module would also monitor the performance of the process located
in the proximity of these loops. The boundaries specified for individual module
responsibilities are likely to overlap one another so their union should encompass the

4
entire plant.
Economies of scale would be achieved if minimal use could be made of
mathematical models. This would facilitate common software that could be
configured at the same time as individual loops were tuned. Clearly these economies
of scale would be diminished if the algorithms were too plant specific and required
knowledge not readily available from the plant.
Such a vision will not be realised easily. Considerable research and debate are
required before practicable implementations evolve. The method described here is a
contribution towards this goal. Its novelty lies in its focus on the control system and
on how it responds, in the steady state, to faults and disturbances in both the control
system and in the local plant. Thus the focus is on distribution. It has many
limitations and issues of implementation have yet to be addressed. However it might
be appropriate for many process plants particularly those under standard single or
cascade PID control. Some of the limitations are as follows:
(1) the controllers themselves must perform to specification and all control loops must
guarantee zero steady state error e.g. because they have integral action;
(2) the fault must remain until a new steady state is reached and that this change in
steady state must be detected;
(3) multiple faults can only be diagnosed if they are separated either spatially, i.e. in
different parts of the plant, or temporarily, i.e. a new steady state is arrived at
before the next fault occurs;
(4) reasoning is performed qualitatively and hence no quantitative results are obtained;
(5) controller outputs, together with measurements of the control variables must be
available as observations.
A likely communications architecture is shown in Figure 1. The detection &
diagnosis modules are called SEVACS to highlight a possible relationship with SEVA
components as described by Henry and Clarke (1993) and by Clarke (1995). Note
that the approach has two components, a distributed component (SEVACS) and a
central component (the FDD Supervisor). In the distributed component, candidate
sets of faults and disturbances would be hypothesised by reasoning qualitatively about
how steady state deviations, observed in the control system, might have been caused.
This reasoning process is based on qualitative equations derived for that particular
form of control system. In the central component, the candidate sets generated by the

5
various SEVACS are then fused by applying various isolation procedures, all of
which take into account known interactions between control systems and sign
information output from the SEVACS.
It is envisioned that this approach would be implemented in two stages, as part of the
(offline) design stage and then, online, during commissioning. At the offline stage the
plant would be decomposed into manageable compartments and each control system
would be considered in turn. The following characteristics would then be
identified/hypothesised for each control system:
its structure, i.e. whether it has a standard form like a single loop or double,
cascade loops or whether a new special form has to be recognised;
the process Type Number (Dorf and Bishop, 1995) (if known);
open loop stability (if known);
steady state gains between interacting loops (if known).
Based on these characteristics, an appropriate configuration would then be
downloaded to each of the detection & diagnosis modules. During commissioning,
online procedures would then be executed to obtain those items above that were still
unknown. In addition the modules would be configured to detect changes in steady
state and the FDD supervisor would be specified.
SEVACS Modules
In the approach proposed here fault isolation is achieved by reasoning about steady
state deviations in measured variables by referring to cause-effect knowledge of
individual control systems. Section 2 analyses how standard control systems respond
to faults and disturbances, in general, by referring to linear control systems theory and
to signed-directed-graph (SDG) representations. Both linear and weakly non-linear
(i.e. linearisable) processes are considered. Section 3 describes the cause-effect
knowledge that can be generated from this analysis, and which can be downloaded to
the various modules. Issues of generality, in terms of the diversity of disturbances
and non-linearities, are discussed in the Conclusions.

Citations
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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.
Abstract: Recently, Maurya et al. (Ind. Eng. Chem. Res. 42 (2003b, c) 4789,4811) have presented a comprehensive framework for signed directed graph-based analysis of process systems where major theoretical results have been substantiated with simple examples or individual unit-based case studies. In this article, two case studies are presented to illustrate SDG-based analysis of process flowsheets containing many units and control loops. While the literature is replete with single unit examples, flowsheet level analysis as described in this paper is virtually non-existent. The first case study deals with prediction of initial response and its fault diagnostic application in the Tennessee Eastman (TE) flowsheet using a lumped parameter model of the process. The second case study deals with the steady-state analysis and fault diagnosis (FD) of a reaction–separation process. For this case study, the overall signed digraph for the process is developed from the digraphs for individual units and control loops in the flowsheet. It is shown that digraph-based steady-state analysis results in good diagnostic resolution.

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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...

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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.

87 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)....

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Journal ArticleDOI
Abstract: Although signed directed graphs (SDG) have been widely used for modeling control loops, due to lack of adequate understanding of SDG-based steady-state process modeling, special and cumbersome methods are used to analyze control loops. In this paper, we discuss a unified SDG model for control loops, in which both disturbances (sensor bias, etc.) as well as structural faults (sensor failure, controller failure, etc.) can be easily modeled under steady-state conditions. Various fault scenarios such as external disturbances, sensor bias, controller failure, etc. have been thoroughly analyzed. A new algorithm for steady-state fault diagnosis using the SDG model for the steady-state system, that uses a combination of forward- and backward-reasoning, is proposed. Three case studies are presented to show the utility of the steady-state SDG model for fault diagnosis. A tank-level control system is used as the first case study. The second case study deals with fault diagnosis of a multi-stream-controlled CSTR. The third case study deals with fault/failure diagnosis in a process flowsheet containing a CSTR with one control loop and a flash vaporizer with three control loops.

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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.

75 citations


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