Diagnosis and Monitoring Method for Detecting and Localizing Bearing Faults - Dahmane, Saida; Berrabah, Fouad; Defdaf, Mabrouk; Salah, Saad (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2024)
Induction motors in modern industry are becoming more and more functional and complex. Unfortunately, these machines are not free from damages what make their fault diagnosis the most critical aspect of system monitoring and maintenance. Vibrational signal data yields relevant information about the state of the entire system, as well as specifically about one of its components that makes its analysis quite interesting. For this effect, the current paper aims to propose an automatic diagnosis and monitoring method for detecting and locating bearing faults in an induction motor based on vibration signal processing. The suggested method combines the discrete wavelet transform (DWT) with the envelope spectrum (ENV) as advanced signal processing, incorporating a machine learning algorithm based on random forest classifier. The discrete wavelet transforms (DWT), using the Haar wavelet, decomposes the vibrational signal to provide both approximations and details. Each detail is then reconstructed to avoid any missing of information. To precisely select the reconstructed detail (?????) that provides pertinent information about bearing faults, a statistical study is conducted. This study involves calculating four indicators (Root mean square (RMS), correlation coefficient (CC), energy coefficient (EC) and peak to peak (P2P) factor) is performed for each (?????). These indicators are compared with threshold indicators, and this criterion is met by the reconstructed details 1 and 3. The obtained reconstructed details are then subjected to the spectral envelope analysis to detect the fault frequencies, which are considered as new features entering the random forest classifier model. This combination of approaches allows better feature extraction and structuring of the dataset, leading to improved accuracy of the random forest classifier, achieving a higher classification rate of more than 99,53 %. The proposed DWT-ENV-RF method indicates well its efficiency when compared to other recent works, and the attained results are all confirmed by the experimental tests conducted in the CWRU laboratory.