A self-validating control system based approach to plant fault detection and diagnosis
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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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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References
237 citations
"A self-validating control system ba..." refers background or methods in this paper
...Figure 2b shows the kind of output that is sought: the temperature data of figure 2a has been converted into a time series of R-statistics (Cao and Rhinehart, 1995, 1997), which in turn has been analysed by applying a hypothesis test to detect if a change has occurred; this has then been automatically interpreted into the more meaningful form shown....
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...Although this problem is not new (see for instance, Cao and Rhinehart, 1995, 1997; Theilliol et al., 1995), it is not clear whether steady state identifiers have been implemented successfully on a large scale plant....
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...Figure 2b shows the kind of output that is sought: the temperature data of figure 2a has been converted into a time series of R-statistics (Cao and Rhinehart, 1995, 1997), which in turn has been analysed by applying a hypothesis test to detect if a change has occurred; this has then been…...
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208 citations
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...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)....
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...Feedback control adds to the complexity of fault detection in process plants by masking measurement deviations that might indicate a fault, and by making it difficult to distinguish between a sensor, actuator, or plant failure (Henry and Clarke, 1993)....
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198 citations
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...…control (MSPC), gross error detection and data reconciliation (Rollins and Davis, 1992; Crowe, 1996; MacGregor and Kourti, 1995; Tong and Crowe, 1995; Albuquerque & Biegler, 1996; Heyen et al., 1996; 8 Schraa and Crowe, 1996; Bagajewicz & Jiang, 1997; Bakshi, 1998; Dunia and Qin, 1998; Luo et al.,…...
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184 citations
166 citations
"A self-validating control system ba..." refers methods in this paper
...…statistical process control (MSPC), gross error detection and data reconciliation (Rollins and Davis, 1992; Crowe, 1996; MacGregor and Kourti, 1995; Tong and Crowe, 1995; Albuquerque & Biegler, 1996; Heyen et al., 1996; 8 Schraa and Crowe, 1996; Bagajewicz & Jiang, 1997; Bakshi, 1998; Dunia and…...
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