Gear Transmission Fault Classification using Deep Neural Networks and Classifier Level Sensor Fusion

Gear transmissions are widely used in industrial drive systems. Fault diagnosis of gear transmissions is important for maintaining the system performance, reducing the maintenance cost, and providing a safe working environment. This paper presents a novel fault diagnosis approach for gear transmissions based on convolutional neural networks (CNNs) and decision-level sensor fusion. In the proposed approach, a CNN is first utilized to classify the faults of a gear transmission based on the acquired signals from each of the sensors. Raw sensory data is sent directly into the CNN models without manual feature extraction. Then, classifier level sensor fusion is carried out to achieve improved classification accuracy by fusing the classification results from the CNN models. Experimental study is conducted, which shows the superior performance of the developed method in the classification of different gear transmission conditions in an automated industrial machine. The presented approach also achieves end-to-end learning that can be applied to the fault classification of a gear transmission under various operating conditions and with signals from different types of sensors.