Roller bearing fault discrimination with harmonic wavelet package and ORO-RVM

摘要

Roller bearing is one of the widely used elements in a rotary machine.The vibration signal of roller bearing reveals the characteristics and feature of roller bearing faults.Extraction feature from vibration signal and discrimination fault condition are indirect means to ensure the safety operation of machine.This paper addresses a novel roller bearing faults discrimination method with harmonic wavelet package and OAO-RVM (One Against One-Relevance Vector Machine).First, decompose vibration signal with harmonic wavelet package and compute the vector energy from wavelet coefficients.The feature vector is prepared after the vector energy has been standardized.Second, the multi-classification model is established with the simplified OAO-RVM for the purpose of identifying good bearing, bearing with inner race fault, bearing with out race fault and bearing with roller fault.Finally, capture the vibration signal from the roller bearing stand of electric engineering lab to illustrate the proposed method.The feature extraction method with harmonic wavelet package is compared with conventional wavelet package.The accuracy and efficiency of three fault discrimination methods are compared.Experiment results show that the proposed feature extraction method is more effective than conventional method.Compared with ORA (One Against Rest)-RVM and DT (Decision Tree)-RVM, the simplified ORO (One Against One)-RVM model is the best fault discrimination method for its accuracy and efficiency.