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Application of neural networks for process fault diagnosis and safety.

School of Chemical Engineering, Purdue University, West LaFayette, Indiana 1995 May:9 pages
A final performance report on neural network fault detection and diagnosis was presented. The research goal was the development of neural networks for the design of chemical process hazard detection, prevention and safety systems. Only highlights of the findings were presented. Previously published supporting material were referenced. Linear activation function based neural nets, using hyperplanes in the input units space, were considered unsuitable for the description of fault space. The use of an ellipsoidal activation function for the hidden nodes generated bounded fault classes without leading to generalization problems. Several related problems involving the number, location and size of the ellipsoids were solved in referenced material. Network, training set and input space decompositions significantly reduced computational complexity and training time by using principal component analysis and hidden nodes specialization to reduce input space. The extraction of relevant qualitative information from noisy data was accomplished by the development of a hierarchical representation to model process trends. Classes of trends from noisy time series were identified using neural network techniques. Applicability of these techniques to process monitoring was tested using computer simulations of a continuous stirred tank reactor and a fluidizied catalytic cracking process system. At 5% to 10% noise levels, the neural net provided noise tolerant fault diagnosis. Noise tolerance was found to depend on the importance of the sensor monitored and the number of other sensors read by the network. The usefulness of a neural network as a diagnostic system for complex, potentially dangerous processes to aid operators in accident prevention was discussed. The author concludes that neural networks are superior to alternatives for many problems in process fault diagnosis.
NIOSH-Grant; Control-technology; Analytical-models; Computer-models; Information-processing; Simulation-methods
Chemical Engineering Purdue University West Lafayette, IN 47907
Publication Date
Document Type
Final Grant Report
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NIOSH Division
Source Name
School of Chemical Engineering, Purdue University, West LaFayette, Indiana
Performing Organization
Purdue University West Lafayette, West Lafayette, Indiana
Page last reviewed: September 2, 2020
Content source: National Institute for Occupational Safety and Health Education and Information Division