Abstract
The traditional qualitative SDG method has a great significance in safety engineering of process industry. But its inherent qualitative ambiguities can usually result in many spurious interpretations about faults because of the absence of quantitative information. On the basis of traditional qualitative SDG, we present a new probabilistic SDG model to describe large-scale systems utilizing probabilistic information. The definition of probabilistic SDG is presented and the inference method is discussed. Fault diagnosis based on the new probabilistic SDG approach can compute the probabilities of each candidate fault given the on-line measured evidence and then sort them by their probabilities. So the faults which will result in the same qualitative symptoms can be distinguished. A real fault diagnose case of the inversion of synthetic ammonia based on probabilistic SDG is given. Experimental results show the validity and advantages of the new probabilistic SDG approach.
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Lü, N., Wang, X. (2007). A Probabilistic SDG Approach to Fault Diagnosis of Industrial Systems. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_58
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DOI: https://doi.org/10.1007/978-3-540-74282-1_58
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