title: Reliable bearing fault diagnosis using Bayesian inference-based multi-class support vector machines.
abstract: This letter presents a multi-fault diagnosis scheme for bearings using hybrid features extracted from their acoustic emissions and a Bayesian inference-based one-against-all support vector machine (Bayesian OAASVM) for multi-class classification. The Bayesian OAASVM, which is a standard multi-class extension of the binary support vector machine, results in ambiguously labeled regions in the input space that degrade its classification performance. The proposed Bayesian OAASVM formulates the feature space as an appropriate Gaussian process prior, interprets the decision value of the Bayesian OAASVM as a maximum a posteriori evidence function, and uses Bayesian inference to label unknown samples.
authors.affiliation: School of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan, South Korea m.m.manjurul@gmail.com, kjy7079@gmail.com, sherazalik@gmail.com, jongmyon.kim@gmail.com.
authors.forename: M M Manjurul
authors.initials: MM
authors.lastname: Islam
authors.affiliation: School of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan, South Korea m.m.manjurul@gmail.com, kjy7079@gmail.com, sherazalik@gmail.com, jongmyon.kim@gmail.com.
authors.forename: Jaeyoung
authors.initials: J
authors.lastname: Kim
authors.affiliation: School of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan, South Korea m.m.manjurul@gmail.com, kjy7079@gmail.com, sherazalik@gmail.com, jongmyon.kim@gmail.com.
authors.forename: Sheraz A
authors.initials: SA
authors.lastname: Khan
authors.affiliation: School of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan, South Korea m.m.manjurul@gmail.com, kjy7079@gmail.com, sherazalik@gmail.com, jongmyon.kim@gmail.com.
authors.forename: Jong-Myon
authors.initials: JM
authors.lastname: Kim
mesh_terms: D000162:Acoustics; D001499:Bayes Theorem; D004868:Equipment Failure; D019544:Equipment Failure Analysis / Q000379:methods*; D008422:Materials Testing / Q000379:methods*; D008962:Models, Theoretical; D009038:Motion; D013016:Sound; D060388:Support Vector Machine; D013997:Time Factors