Multi Sensor Data Fusion based Gear Fault Diagnosis using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Objective: To develop a methodology based on multi sensor data fusion approach which combined vibration and sound signals to identify the gearbox faults using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Method: The vibration and sound signals acquired from a gearbox are decomposed into a number of IMFs using CEEMDAN. Best IMF is selected based on proposed fault index and the statistical parameters are extracted from the IMFs of vibration and sound signals for different simulated faults. Principal Component Analysis (PCA) is implemented in order to select the best features. K-nearest neighbor classifier is used to demonstrate the classification accuracy. Findings: Initially statistical parameters were extracted for raw vibration and sound signals in order to obtain the fault severity. But due to uneven trend these were failed to reveal the fault information with higher accuracy. CEEMDAN based feature sets provide good diagnosis results due to its capability to decompose signal into different higher to lower frequency modes called IMFs. Hence, it is concluded that the proposed method has the ability to extract the gearbox fault characteristic and diagnose the severity of fault. Sensitive IMF selection is a paramount to swift the overall performance of a diagnosis system. Improvement: The results show that the data fusion approach and combination of CEEMDAN and PCA techniques employs the advantages traits of one or the other technique to generate overall improvement in diagnosing severity of local faults.