A hybrid intelligent system for fault detection and sensor fusion
Applied Soft Computing
Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference
Expert Systems with Applications: An International Journal
Fault diagnosis of power transformer based on support vector machine with genetic algorithm
Expert Systems with Applications: An International Journal
Feature generation using genetic programming with application to fault classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In order to ensure the safety in petrochemical production, it is of great importance for key equipment in petrochemical production unit to maintain, monitor and diagnose fault. Non-dimensional indicator is insensitive to the change of working condition so it is applicable to fault diagnosis technology, yet it is not satisfactory for complex concurrent fault diagnosis in rotating machinery and the number of conventional non-dimensional indicators is small. Building new non-dimensional indicator, which especially possesses the features of integrated diagnosis, is very significant to improve capability to diagnose fault. In the paper, the method of building new non-dimensional indicator is presented. Based on the previous research findings about 3MGCA to search arithmetical combination of conventional non-dimensional indicator, new non-dimensional indicator is acquired using optimisation algorithm of measured fault data. Experiments show that the proposed method is practical and efficient for building new non-dimensional indicator in rotating machinery, and it has good potential application in the engineering practice.