Automated function generation of symptom parameters and application to fault diagnosis of machinery under variable operating conditions

Dimensional or nondimensional symptom parameters are usually used for condition monitoring of plant machinery. However, it is difficult to extract the most important symptom parameters and the functions of those parameters by which machinery faults can be sensitively detected and the fault types can be precisely distinguished. In order to overcome this difficulty and to ensure highly accurate fault diagnosis, a new method, called "automated function generation of symptom parameters" using genetic algorithms (GA) is presented in this paper. By applying the method to real machinery diagnosis problems, it has been shown that the key symptom parameter function can be quickly generated. We give a diagnosis example of rolling bearings whose operating conditions are variable in terms of rotation speed and load.