Fault diagnosis observer-based

To face these faults, fault diagnosis modules are developed. They are based on the observations and interpretations of symptoms, i.e. the changes of an observable quantity from normal behavior. Fault Diagnosis task can be split in three main steps. 

Chapter 6 is focused on fault diagnosis methods for chemical batch processes. Consistent with the approach followed in Chap. 5, the focus of the chapter is on model-based techniques and, in particular, on techniques based on the use of state observers. 

As concerns the former, statistical tests on the measured data are usually adopted to detect any abnormal behavior. In other words, an industrial process is considered as a stochastic system and the measured data are considered as different realizations of the stochastic process. The distribution of the observations in normal operating conditions is different from those related to the faulty process. Early statistical approaches are based on univariate statistical techniques, i.e., the distribution of a monitored variable is taken into account. For instance, if the monitored variable follows a normal distribution, the parameters of interest are the mean and standard deviation that, in faulty conditions, may deviate from their nominal values. Therefore, fault diagnosis can be reformulated as the problem of detecting changes in the parameters of a stochastic variable [3, 30],... 

The literature focused on model-based FD presents a few applications of observers to chemical plants. In [10] an unknown input observer is adopted for a CSTR, while in [7] and [21] an Extended Kalman Filter is used in [9] and [28] Extended Kalman Filters are used for a distillation column and a CSTR, respectively in [45] a generalized Luenberger observer is presented in [24] a geometric approach for a class of nonlinear systems is presented and applied to a polymerization process in [38] a robust observer is used for sensor faults detection and isolation in chemical batch reactors, while in [37] the robust approach is compared with an adaptive observer for actuator fault diagnosis. 

P. Kabore, S. Othman, T.F. McKenna, and H. Hammouri. Observer-based fault diagnosis for a class of nonlinear systems—application to a free radical copolymerization reaction. International Journal of Control, 73 787-803, 2000. 

O.A.Z. Sotomayor and D. Odloak. Observer-based fault diagnosis in chemical plants. Chemical Engineering Journal, 112 93-108, 2005. 

The fault diagnosis and isolation scheme is based on a knowledgebase developed beforehand. This knowledgebase stores the symptoms associated with faults and serves as lookup table from which faults corresponding to observed symptoms can be extracted. For construction of the knowledgebase, fault is assumed in each parameter of the system (both [+] and [—] faults) and the change in qualitative values of the measured variables is explicitly calculated using qualitative operators. 

This study is about the fault diagnosis by the hybrid method of qualitative model-based method and quantitative history data-based method. The diagnosis is performed by the observation of measured value and predicted value by DPLS model built on the local causal relationships of SDG The proposed method has the advantages to improve diagnosis accuracy and resolution, and facilitate diagnosis of masked multiple-fault. 

System diagnosis frequently lies on a model that represents the normal behavior of a particular process to be supervised. The fundamental problem comes then from the inaccuracies associated with the model, either related to the ignorance of the kinetics or its parameters, or related to the ignorance of its inputs. Within the framework of this chapter, the interest is focused on the detection and location of sensor faults in the presence of unknown inputs. Among the existing solutions based on observers, one can distinguish the approaches based on non-linear unknown inputs observers (see for example, [21],... 

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],... 

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