Novel Adaptive Sparse-Spike Deconvolution Bearing Fault Detection Method Based on Curvelet Transform

• Published in: IEEE access Vol. 9; pp. 6239 - 6249
• Main Authors: Li, Yanfeng, Wang, Zhijian, Zhao, Tiansheng, Zhao, Yuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Curvelet transform; Deconvolution; Fault detection; Fault diagnosis; Feature extraction; L1 norm regularization; Parameters; Particle swarm optimization; Pulse amplitude; quantum-behavior particle swarm optimization algorithm; Regularization; Regularization methods; Roller bearings; Spikes; Transformations (mathematics); Transforms; Vibration; Vibrations
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3048127

This paper has proposed a novel bearing fault detection method about adaptive Sparse-spike Deconvolution based on Curvelet Transform (CTSSD), where the novel technique about adaptive Sparse-spike Deconvolution names after ASSD. Its purpose is to recover the pulse sequence from a vibration signal including complex noise, and to evaluate the periodic pulse position and pulse amplitude. Firstly, in order to make the results sparse and improves the stability of the result, the L1 norm regularization method is proposed in this paper, which is used to constrain the signal pulse sequence sparsely. Secondly, considering that regularization parameters are not adaptive, the Quantum behavior Particle Swarm Optimization (QPSO) algorithm is proposed to determine the optimal regularization parameters, adaptively. Finally, considering that the periodic features of ASSD extraction are not continuous, the Curvelet transform is further introduced. The fault signal is transformed into the Curvelet domain, and the Curvelet coefficient is used to characterize the fault signal pulse sequence. This method proposed in this paper is applied to the simulation signal and the vibration signal of rolling bearing fault, and is compared with the ASSD and the minimum entropy deconvolution (MED) to verify the reliability and effectiveness.