Abstract: At present most of fault diagnosis systems are dedicated to fault detection, fault elimination and fault effect analysis with specific diagnosis approaches. Even though fault effect based on these approaches concerns the affected components as well as functions. However, there is no analysis of the still available functions which could continue to operate despite the failure. Currently, there are few approaches working on the fault analysis based on the existing fault diagnosis system. To support a full fault diagnosis, this paper proposes a novel full fault effect analysis approach based on system models. Within a defect component in an automation system, available functions can be identified by the presented approach. The presented approach uses the results of the existing fault diagnosis system as an analysis basis, e.g. the fault ID and the fault location. The propagation of a fault is identified with the help of the requirement-function-component models, which are provided by the

• Publication Date: 11-Oct-2015
• DOI: 10.15224/978-1-63248-064-4-18
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Abstract: For signal processing in sensor networks there is an on-going challenge for filling missing information when it is either incomplete, uncertain or biased, in ways that are both efficient and with confidence. This paper reviews three established and additional newly developed techniques addressing the problem. Considering sensor signals that are highly correlated in a sensor network, one sensor measurement can be reconstructed based on measurements from other sensors. In such cases, three signal reconstruction methods are considered: 1) principal component analysis (PCA) based missing value approach; 2) self-organizing map neural network (SOMNN) based algorithm; and 3) an analytical optimization (AO) technique. To demonstrate the efficacy of the methods, temperature data are studied on an industrial gas turbine system, where, especially, a faulty sensor signal is utilized to be reconstructed from the other sensor measurements.

• Publication Date: 11-Oct-2015
• DOI: 10.15224/978-1-63248-064-4-19
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• Downloads: 0