Vibration fault diagnosis based on stochastic configuration neural networks

This work presents a study on fault diagnosis in vibration signal processing. Rather than building a fault model through frequently used approaches to handling the series data such as LSTM or hidden Markov field, this work processes the vibration signal by moving the time window to generate multiple samples and then transfers fault diagnosis into a traditional supervised learning problem. Stochastic configuration neural network (SCN) which gives a clear condition of guaranteeing high performance of randomly weighted neural networks is selected as the model for training and testing. Different classifiers are used to conduct a performance comparison, and their comparative advantages including why SCN particularly suitable for this type of learning and more discussions about the experimental results are shown. The paper provides a new scheme to processing vibration signal for fault diagnosis and some useful guidelines of building an appropriate model with high performance.