Gear Fault Diagnosis Based on Adaptive Time-Frequency Feature Extraction and Bsa-Svm Method

There are many types of approaches to the detection of gear faults. The method of a novel adaptive feature ex-traction and optimized machine learning is applied to diagnose a gear faults. Firstly, the vibration signal produced by local faults is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition (LCD) method and initial feature vector matrices are obtained. Then, singular value decomposition (SVD) techniques to obtain singular values extract fault feature values, while avoiding the selection of reconstruction parameters. Secondly, the Backtracking Search Optimization Algorithm (BSA), an evolutionary algorithm, is pro-posed and demonstrated to be effective though various benchmark problems. The paper proposes an optimization method for the SVM parameters based on BSA, being so called BSA-SVM. This is a new approach method applied in diagnosing gear faults. The experimental results prove that the proposed method operates highly effective and mostly feasible for identifying gear faults in practice. By applying this method, the results will be more accurately and shorter time cost