Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition

By V. Purushotham, S. Narayanan, S. A. Prasad. 2005

301 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/a367d92355963dd356bc5c210615387ecbd19c3e

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In Mechanical Engineering

1. Ahmad Rafsanjani

Citations in the corpus

• Nonlinear dynamic modeling of surface defects in rolling element bearing systems (2009, background): “A lot of research work has been published recently, on the detection and diagnosis of bearing defects [5–7].”
• Wavelets for fault diagnosis of rotary machines: A review with applications (2014, unknown): “wavelet coefficients at Mel-frequency scales [90].”
• A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm (2017, methodology): “Purushotham et al. (2005) proposed a new multi-fault pattern recognition method for detecting localized bearing defects based on discrete wavelet transform and hidden Markov model.”