A sensor fusion based approach for bearing fault diagnosis of rotating machine

Fault diagnosis in rotating machines plays a vital role in various industries. Bearing is the essential element of rotating machines, and early fault detection can reduce the maintenance cost and enhance machine availability. In complex industrial machinery, a single sensor has a limitation to capture complete information about fault conditions. Hence, there is a need to involve multiple sensors to diagnose all possible fault conditions effectively. In such situations, an efficient fusion of information is required to develop a reliable fault diagnosis system. In this work, a feature fusion approach is implemented using two different sensors, that is, a contact type vibration sensor and a non-invasive thermal imaging camera. Hilbert transform is applied to decompose raw vibration and thermal image data, and subsequently, features are extracted and fused into a single feature vector. However, the features are fused in a concatenation manner, but this stage has high dimensionality. Neighborhood component analysis (NCA) is applied to reduce this high dimensionality of the feature vector, followed by a relief algorithm (RA) to compute the relevance level to find the optimal features. Finally, these optimal features are used as an input feature vector to the support vector machine (SVM) to classify the faults. The proposed approach resulted in considerably improved classification accuracy and detection quality than individual sensors. Also, the relevance of the proposed approach is proved by comparing its performance with other prevalent feature fusion techniques.