Detection of Local Gear Tooth Defects on a Multistage Gearbox Operating Under Fluctuating Speeds Using DWT and EMD Analysis

Contemporary fault diagnosis algorithms constitute advanced signal processing techniques integrated with the data-driven feature classification algorithms which make an effective fault diagnosis scheme for rotating machinery such as gearboxes and motors. Feature extraction is a prevalent task which is intended to assist the fault diagnosis process by eliciting a set of condition indicators (features) from the input raw signal. In actual scenario, the gearboxes may have multiple stages and are rather operating under fluctuating speeds. The feature extraction technique employed at medium and high ranges of operating speed may not be adequate during low operating speeds. In this present study, the feature extraction abilities of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in terms of their relative effectiveness while ascertaining the local gear tooth defects of a multistage gearbox are compared. Two local gear tooth defects, namely root crack and tooth chip with three severity levels, are seeded artificially. The experiments are carried out on a three-stage spur gearbox experiencing fluctuating operating speeds. Vibration analysis is performed, and the recorded raw vibration signatures are decomposed using DWT and EMD analyses separately. Mother wavelet selection is done using the criteria of energy-to-Shannon entropy ratio. The identification of intrinsic mode functions (IMFs) is made by examining the Pearson correlation coefficient. Various descriptive statistics are obtained from the wavelet coefficients and IMFs and the potential indices among them are chosen by implementing the decision tree algorithm. Finally, support vector machine (SVM) algorithm is executed to distinguish among the various defect severity levels. It has been observed that the SVM in conjunction with DWT has resulted in better classification than SVM in conjunction with EMD.

Automated summary

Contemporary fault diagnosis algorithms utilize advanced signal processing techniques and data-driven feature classification algorithms to provide effective fault diagnosis schemes for rotating machinery. Feature extraction is crucial, and the effectiveness of different feature extraction techniques may vary depending on the operating speeds.