Fault Diagnosis of Gearbox based on ITD-Tunable Q-Factor Wavelet Transform

Indian Journal of Pure & Applied Physics

article

Jay Govind Verma¹  Shivdayal Patel¹  P K Kankar² 

[1] Mechanical Engineering Discipline, PDPM Indian Institute of Information Technology Design & Manufacturing;Discipline of Mechanical Engineering, Indian Institute of Technology

关键词: Fractal feature;    Gearbox;    Machine learning;    Tunable Q-wavelet transforms;    Article;   

来源: National Institute of Science Communication and Information Resources

【 摘 要 】

Gearboxes are an important part of the mechanical drives element that provides the several applications like automotive industry, wind turbine industry and power plant industry, etc. The condition monitoring of the gearbox reduces its operational cost, maintenance cost and avoid hazardous losses. The features selected for the health status of the gearbox has important parameter to calculate classification accuracy. In the current study the intrinsic time-scale decomposition (ITD) and tunable Q-factor wavelet transform (TQWT) are used to diagnose the faults in the gear. The ITD method decomposed the input signal into the baseline signal with instantaneous parameters of signal and sequence of the proper rotation components (PRCs). The PRC of higher kurtosis value is the input signal for TQWT. The TQWT is a discrete wavelet transform and decomposed the vibration signals of the gearbox into sub-bands. The feature vector is calculated for each sub-band of the TQWT. The proposed approach is analyzed by the classification accuracy of the feature vector. The recommended method is evaluated using experimental data of 2009 PHM Data of gearbox under various health conditions. The SVM and KNN methods are investigated that the improved classification accuracy with ITD-TQWT model are 97.9% and 96.9% respectively.

【 授权许可】

Unknown