This paper describes analyses of case studies on failure of structural components in power plants using hierarchical (multilayer) neural networks. Using selected test data about case studies stored in the structural failure database of a knowledge-based system, the network is trained: either to predict possible failure mechanisms like creep, overheating (OH), or overstressing (OS)-induced failure (network of Type A), or to classify a root failure cause of each case study into either a primary or secondary cause (network of Type B). In the present study, the primary root cause is defined as “manufacturing, material or design-induced causes,” while the secondary one as “not manufacturing, material or design-induced causes, e.g., failures due to operation or mal-operation.” An ordinary three-layer neural network employing the back propagation algorithm with the momentum method is utilized in this study. The results clearly show that the neural network is a powerful tool for analyzing case studies of failure in structural components. For example, the trained network of Type A predicts creep-induced failure in unknown case studies with an accuracy of 86 percent, while the network of Type B classifies root failure causes of unknown case studies with an accuracy of 88 percent. It should be noted that, due to a shortage of available case studies, an appropriate selection of case studies and input parameters to be used for network training was necessary in order to attain high accuracy. A collection of more case studies should, however, resolve this problem, and improve the accuracy of the analyses. An analysis module for case studies using the neural network has also been developed and successfully implemented in a knowledge-based system.
Analyses of Possible Failure Mechanisms and Root Failure Causes in Power Plant Components Using Neural Networks and Structural Failure Database
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Yoshimura, S., and Jovanovic, A. S. (May 1, 1996). "Analyses of Possible Failure Mechanisms and Root Failure Causes in Power Plant Components Using Neural Networks and Structural Failure Database." ASME. J. Pressure Vessel Technol. May 1996; 118(2): 237–246. https://doi.org/10.1115/1.2842186
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