Motor Fault Prediction Based on Fault Feature Extraction and Signal Distribution Optimization

The fault prediction of motors can effectively reduce the occurrence of accidents and change the post diagnosis to prevention. However, the systematic errors caused by the complex signal components, such as different kinds of randomly distributed noise, make the false report or misreport inevitable, no matter what prediction model is selected. To tackle this issue, a motor fault prediction method based on fault feature extraction and signal distribution optimization is proposed in this article. A time-frequency parameter and resolution adaptive algorithm (TF-PRAA) is proposed to optimize the raw signal while reserving the fault characteristics. An extended model is developed to decompose and reconstruct the processed signals. Then, the optimized time series is transformed into the signal distribution. Fault prediction is carried out by combining the signal distributions as the inputs of the gated recurrent unit (GRU). Two datasets collected from experiments and National Aeronautics and Space Administration (NASA) are used to validate the effectiveness of the proposed methods. The test results indicate that the proposed methods provide better performance than other state-of-the-art models since the unrelated components of the signal are accurately reduced and the concentration of the signal is improved. From the predictive theory point of view, achieving accurate prediction of such signals is much easier. The method can accurately fulfill both long- and short-term fault prediction tasks.