Adaptive Convolution Sparse Filtering Method for the Fault Diagnosis of an Engine Timing Gearbox

Abstract

Due to the superior robustness of outlier signals and the unique advantage of not relying on a priori knowledge, Convolution Sparse Filtering (CSF) is drawing more and more attention. However, the excellent properties of CSF is limited by its inappropriate selection of the number and length of its filters. Therefore, the Adaptive Convolution Sparse Filtering (ACSF) method is proposed in this paper to implement an end-to-end health monitoring and fault diagnostic model. Firstly, a novel metric entropy–time function (He−T) is proposed to measure the accuracy and efficiency of signals filtered by the CSF. Then, the filtered signal with the minimum He−T is detected with particle swarm optimization. Finally, the failure mode is diagnosed according to the envelope spectrum of the signal with minimum He−T. The effectiveness and efficiency of the ACSF is demonstrated through the experiment. The results indicate the ACSF can extract the failure characteristic of the gearbox.

Gearbox Condition Monitoring and Diagnosis of Unlabeled Vibration Signals Using a Supervised Learning Classifier

“…Among these, vibration signals are the most widely used because they contain a lot of information from inside the mechanical equipment. In order to monitor gearbox conditions and detect defects early, various technologies such as artificial intelligence and signal processing are being researched [4][5][6][7][8][9][10][11][12][13] It is crucial to maintain desirable performance in industrial processes where a variety of faults can occur. For most industries, FDD is an important control method because better processing performance is expected from improving the FDD capability.…”