A Two-Stage Neural Network Classifier for Condition-based Maintenance in Wireless Sensor Networks

摘要

Motor failures in aerospace applications can lead to serious compromises in safety, overall effectiveness, and maintenance costs. In mission critical 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. Three key contributions are outlined in this paper. First, we develop a low cost test bed for simulating bearing faults in a motor. Second, we develop a wireless sensor module for collection of vibration data from the test bed. Finally, we use a novel two stage neural network to classify various bearing faults using the Generalized Hebbian Algorithm (GHA) in the first stage and a supervised learning vector quantization network (SLVQ) with a self organizing map approach for fault classification in the second stage.