Study on fault identification of mechanical dynamic nonlinear transmission system

• Published in: Nonlinear engineering Vol. 10; no. 1; pp. 518 - 525
• Main Authors: Guo, Erfu, Jagota, Vishal, Makhatha, Mamookho Elizabeth, Kumar, Pawan
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 01.01.2021; Walter de Gruyter GmbH
• Subjects: Classification; Decision making; Fault detection; Fault diagnosis; Fault identification; Feature extraction; Kernels; Mechanical drives; Mechanical power transmission system; nonlinear dynamics; Power transmission; Powertrain; Random noise; Steam turbines
• DOI: 10.1515/nleng-2021-0042
• ISSN: 2192-8010

To solve the problems of large mechanical powertrain such as complex structure, serious accident, strong nonlinear characteristics of running state, bad operating environment, non-Gaussian noise, and various uncertain factors, it is difficult to make an accurate fault diagnosis. This paper proposes a method for dealing with nonlinear characteristics using nuclear waves, as well as a system, deeply conducted nuclear base fault feature extraction, classification, and decision making, such as nuclear base state trend prediction technology research, focusing on exploring and improving the accuracy of fault diagnosis under nonlinear conditions, technical method, and way to state prediction accuracy. It offers effective technical assistance for the advancement and use of mechanical power train monitoring and diagnosis technology. A fault detection method based on kernel method is proposed. Based on the characteristics of this method in dealing with nonlinear problems, the research on kernel feature extraction, kernel fault classification and decision making, and kernel state trend prediction are carried out systematically. The experimental results show that the simulation analysis of typical chaotic time series prediction and the application of the operation state prediction of a certain ship main steam turbine unit have achieved good results, among which the average relative error of the single-step prediction of the unit state is 1.7881%, and the average relative error of the 30-step prediction is 3.3983%. Proved that the nuclear methods systematically applied to mechanical power transmission system fault diagnosis and state prediction, effectively enhancing some traditional methods and techniques dealing with nonlinear feature extraction, the nonlinear prediction capability for fault identification, and nonlinear state, to deal with nonlinear fault diagnosis problems of engineering practice, a large number of explored effective solution.