Early detection of defects in bearings is essential to avoid the complete failure of machinery and the associated costs. This study presents a novel method for fault diagnosis of bearings using sensor fusion with a microphone and an accelerometer. The system has five modules, namely data acquisition, signal processing, feature extraction, classification and decision-making. A test-rig is designed to collect acoustic and vibration signals. Then, for each signal, indices are calculated in the time and frequency domains. After using principal component analysis (PCA) for feature extraction, the k-nearest neighbours (kNN) method is used in the classification module. Finally, a decision on the kind of fault and its size is made based on the decision fusion module. The aim of this study is to propose a fusion method to improve the effectiveness and reliability of bearing defect diagnosis compared to what can be achieved with vibration or acoustic measurements alone. The results obtained from this preliminary study show that condition monitoring using the accelerometer is the more effective technique for determining the type of fault, while the microphone is effective for classifying the size of fault. Experimental results also confirm these findings.

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