Fault diagnosis depends largely on feature analysis of vibration signals. However, feature extraction for fault diagnosis is difficult because the vibration signals often contain a strong noise component. Noises stronger than the actual fault signal may interfere with diagnosis and ultimately cause misdiagnosis. In order to extract the feature from a fault signal highly contaminated by the nois...

Purpose – To present a new application of Pursuit based analysis for diagnosing rolling element bearing faults. Methodology Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique-based fault detection and identification. This paper presents a compar...

Elastohydrodynamically lubricated (EHL) contacts are fundamental for several rotating machine elements. For example gears, rolling element bearings and lubricated chain drives work due to the principle of EHL. All of these machine elements require maintenance and, as condition based maintenance has increased in industry, the need for monitoring techniques has also increased. In order to avoid i...

The rolling element bearingsare most critical components in a machine. Condition monitoring and fault diagnostics of these bearings are of great concern in industries as most rotating machine failures are often linked to bearing failures. This paper presents a methodology for fault diagnosis of rolling element bearings based on discrete wavelet transform (DWT) and wavelet packet transform (WPT)...