Gearbox fault diagnosis method based on PSOOBP-CS algorithm

Abstract

The invention provides a gear box fault diagnosis method based on a PSOOBP-CS algorithm, which is characterized in that gear box fault feature data is extracted based on ICEEMDSN and MPE algorithms, existence of intermittent components is judged firstly, and a corresponding decomposition algorithm is adopted accordingly, so the operation efficiency is improved, and the residue of white noise added in the signal decomposition process by only adopting the ICEEMDAN algorithm in the traditional feature extraction method is reduced. According to the invention, test data is collected through a gear case working condition data collection device and transmitted to a fault diagnosis unit in a computer, an Elman-TCL neural network stored in the fault diagnosis unit is optimized by using a PSOOBP-CS algorithm, the search capability of PSO and the global search capability of CS are superposed, the particle speed and position in the PSO algorithm are optimized, and the particle global optimal position of the PSO algorithm is optimized, so the defects of premature convergence and easy falling into local optimum are overcome, and the fault diagnosis effect is good.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis, in particular to a gearbox fault diagnosis method based on PSOOBP-CS algorithm. Background technique [0002] As the main component of rotating machinery, gearboxes are widely used in wind power, heavy industry, automobiles, people's livelihood and other fields. Once the gearbox fails, it will bring unpredictable consequences, affect the operation of the equipment, bring large production costs and time costs to the enterprise, cause huge economic losses, and even endanger people's lives. Therefore, real-time diagnosis of the health status of the gearbox is of great significance for maintaining the health of equipment and ensuring the safety of people's lives. [0003] With the continuous development of modern science and technology, the research on gearbox fault diagnosis methods has become more and more abundant. The analysis methods include time-domain analysis, frequency-domain analys...

AI Technical Summary

Problems solved by technology

Among them, for the method of feature extraction, at present, the parameter indicators in the time domain and frequency domain of the signal are extracted as features to identify faults, and the means are relatively simple. Therefore, it is necessary to conduct in-depth research and continuous improvement on the method of feature extraction

At the same time, the neural network, vector machine and other models used in traditional pattern recognition methods have limitations, which have a great impact on the diagnostic accuracy of the results.

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