Fault redundancy

Simplex, no fault Redundancy Error Detection - Multiversion... 

Fathi, Z., W.F. Ramirez, and J. Korhicz, Analytical and Knowledge-Based Redundancy for Fault Diagnosis in Process Plants, AlChE Journal, 39(1), 1993, 42-56. (Fault diagnosis)... 

Thus, the fault tree of Figure 3.4.4-4 is represented in mincut form as T =C+A B and by fault tree as Figure 3.4.4-5 which shows the remarkable simplification and reduction provided by the mincut form. This shows that the top event, T, consists of a single component failure, C, and a doubly-redundant failure. 

Testing schemes generally affect complete subsystems hence, consideration of each hardware element is unnecessary. Tests of redundant portions of a system are particularly important, and may be constrained by the technical specifications which must be reflected in the fault tree. Testing may require the reconfiguration of systems for the test, which may prevent the performance of their designed function. In this case, other members of the redundancy must be available, but may fail. Failure to restore a system after test significantly increases the risk. 

Initially, a system s hierarchy is identified for subsystems, sub-subsystems and so on to the components for which data must be found. The top event specifies system failure subsystems required for operation of the system in the mode specified are input to the top event s OR gate. Redundancy is represented by the redundant systems inputting an AND gate. This process of grouping subsystems under OR gates, if they can individually fail a function, or under AND gates if concurrent failures are necessary, is continued to the component or support system level until the tree is completed. This process grades the hierarchy from top to bottom, down the fault tree. 

The simplicity of the final result is the mincut representation (sum of products Section 2.2) depicted as a fault tree in Figure 3.4.4-9. If the single double, and so on to higher redundancy components had been identified, the complex and awkward tree of Figure 3.4.4-S would have been avoided. Some systems are so complex that this cannot be done by observation, but computer analv. is will show simplicities if they exist. 

Direct Introduction - This method, similar to the preceding, introduces the dependency in the. fault tree higher than the affected subsystems to bypass the "AND" operation of the redundancy. This is also the technique in WASH-1400. An example of this technique is found in the fault tree shown in Figure 3.4.4-9. 

A traditional approach to fault diagnosis in the wider application context is based on hardware i.e. physical) redundancy methods which use multiple lines of sensors, actuators, computers and software to measure and/or control a particular variable. Typically, a voting scheme is applied to the hardware redundant system to decide if and when a fault has occurred and its likely location amongst redundant system components. The use of multiple redundancy in this way is common, for example with digital fly-by-wire flight control... 

In analytical redundancy schemes, the resulting difference generated from the consistency checking of different variables is called a residual signal. The residual should be by convention zero-valued when the system is normal and should diverge from zero when a fault occurs. This zero and non-zero property of the residual is used to determine whether or not a fault has occurred. Analytical redundancy makes use of a quantitative model of the monitored process and is therefore often referred to as the model-based approach to fault diagnosis. 

The presented approach has been applied on the process illustrated in Figure 8. In this case, several physical redundancy relations are available but the same method can be successfully applied with a standard instrumentation fulfilled by software sensors [17]. r, T2, rs, re, rr and ril are based on physical redundancy, r4 and rs on functional redundancy, rg, rg and riO are derived from dynamical modeling. As expressed in Table 2, there is not one single residual that is representative of a specific fault. A diagnosticability analysis of the signature table shows that among the 256 different states of the system ... 

Supply one ESP with a redundant or spare mechanical/electrical section, this approach protects the FCC from being brought down should some degree of internal fault occur due to insulator breakage, full hopper, and so on. 

An interesting additional feature of using potentiometric sensors in array mode has been pointed out, which is to profit from the extra information to gain confidence in measurements, that is to perform redundant analysis. This strategy can be the basis for automated fault detection of the sensory elements [48] and also be an aid for more robust calibrations [49]. 

Reliability Large numbers of redundant sensors allow signal averaging to improve accuracy and the use of fault detection algorithms to detect the failure of individual array elements.36 For example, the variance of a measurement based on the average of N identical sensors is... 

Early approaches to fault diagnosis were often based on the so-called physical redundancy [11], i.e., the duplication of sensors, actuators, computers, and softwares to measure and/or control a variable. Typically, a voting scheme is applied to the redundant system to detect and isolate a fault. The physical redundant methods are very reliable, but they need extra equipment and extra maintenance costs. Thus, in the last years, researchers focused their attention on techniques not requiring extra equipment. These techniques can be classified into two general categories, model-free data-driven approaches and model-based approaches. 

In this chapter, an FD framework for batch chemical processes is developed, where diagnosis of sensor, actuator, and process faults can be achieved via an integrated approach. The proposed approach is based on physical redundancy for detection of sensor faults [38], while an analytical redundancy method, based on a bank of diagnostic observers, is adopted to perform process/actuator fault detection, isolation, and identification [4],... 

J. Gertler. Analytical redundancy methods in fault detection and diagnosis. In Proceedings of IFAC SAFEPROCESS Symposium, pages 9-21,1991. 

In the fifth chapter, a general overview of temperature control for batch reactors is presented the focus is on model-based control approaches, with a special emphasis on adaptive control techniques. Finally, the sixth chapter provides the reader with an overview of the fundamental problems of fault diagnosis for dynamical systems, with a special emphasis on model-based techniques (i.e., based on the so-called analytical redundancy approach) for nonlinear systems then, a model-based approach to fault diagnosis for chemical batch reactors is derived in detail, where both sensors and actuators failures are taken into account. 

In a computerized plant, it is best to avoid putting redundant inputs on or in the same slot, cluster, or board in the computer. The safety system should be continuously exercised. An independent safety system suffers from a major drawback, particularly if it is implemented using relays. This drawback is that the safety system usually remains inert for long periods of time. Electronic circuits or relays are subject to failures such that when the safety system is called on to operate, it may be incapable of doing so. The best way to ensure that the safety system has not suffered a fault is to continuously exercise it. This is best done if the safety system is also responsible for control calculations and is constantly in use (Wensley, 1986). 

And model-based methods which are composed of quantitative model-based methods (such as analytical redundancy (Chow and Willsky, 1984), parity space (Gertler and Singer, 1990), state estimation (Willsky, 1976), or fault detection filter (Franck, 1990)) and qualitative model-based methods (such as causal methods digraphs (Shih and Lee, 1995), or fault tree (Venkatasubramanian, et ah, 2003)). 

Several of FM s Loss Prevention Data Publications (1, 17B, 17C) discuss the concept of triply-redundant, fault-tolerant, high-reliability hardware/software systems for manufacturing operations. Risk analysis and systems reliability research is currently underway to develop better guidelines for the design and application of reliable process control systems. 

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