Extraction of operation characteristics in mechanical systems using genetic morphological filter

Operation characteristics such as rotating speed are of great importance in condition monitoring and fault diagnosis of rotating machineries. Since different components in the mechanical system are often correlated and interacted, the acquired signals are highly coupled and contaminated by lots of high-frequency noises. As a result, the frequency and phase of the observed signal cannot reflect actual condition of the mechanical component. In this paper, we propose a genetic morphological filter to purify the operation characteristics of the mechanical system in the time domain. Firstly, an average weighted combination of open-closing and closeopening morphological operator, which eliminates statistical deflection of amplitude, is utilized to remove stochastic noises from the original signal. Then, according to the geometric characteristic of the noises, the structure elements are constructed with two parabolas and four parameters of the structure elements are synchronously optimized with genetic algorithm. The combination of Hurst exponent and Kurtosis is selected as the fitness function of the genetic algorithm and the optimal parameters of the structure elements correspond to the maximum of fitness function. The proposed method is evaluated by simulated signals with different frequencies, vibration signals measured on condensate pump and sound signals acquired from motor engine, respectively. Results show that with genetic morphological filter, the operation characteristics such as rotating speed and phase can be extracted in the time domain efficiently.