Skip to Main Content
This paper has three contributions. First, we develop a low-cost test-bed for simulating bearing faults in a motor. In Aerospace applications, it is important that motor fault signatures are identified before a failure occurs. It is known that 40% of mechanical failures occur due to bearing faults. Bearing faults can be identified from the motor vibration signatures. Second, we develop a wireless sensor module for collection of vibration data from the test-bed. Wireless sensors have been used because of their advantages over wired sensors in remote sensing. Finally, we use a novel two-stage neural network to classify various bearing faults. The first stage neural network estimates the principal components using the generalized Hebbian algorithm (GHA). Principal component analysis is used to reduce the dimensionality of the data and to extract the fault features. The second stage neural network uses a supervised learning vector quantization network (SLVQ) utilizing a self organizing map approach. This stage is used to classify various fault modes. Neural networks have been used because of their flexibility in terms of online adaptive reformulation. At the end, we discuss the performance of the proposed classification method.