Rolling Element Bearing Fault Diagnosis by Different Data Fusion Techniques

Buchaiah, Sandaram; Shakya, Piyush

doi:10.1007/978-981-15-5701-9_28

Cited by 1 publication

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Incipient fault detection and health state assessment depend on efficient degradation indicators computed from bearing condition monitoring data. The vibration signals are mainly used in bearing health monitoring due to their high sensitivity to change (Buchaiah and Shakya, 2021; Tiwari and Upadhyay, 2021). Usually, bearings work under fluctuating loads and speeds.…”

Section: Introductionmentioning

confidence: 99%

Automatic incipient fault detection and health state assessment of rolling element bearings using pruned exact linear time method

Buchaiah

Shakya

2022

Journal of Vibration and Control

Self Cite

View full text Add to dashboard Cite

Rolling element bearing is a crucial component in rotating machines. The existence of faults in the bearing causes sudden failures, resulting in catastrophic failure of machines. Incipient fault detection and health state assessment are the essential tasks in condition-based maintenance to avoid machine failures. This paper proposes a new method, the Pruned Exact Linear Time, for identifying the incipient faults and following damage states in the bearing. When a fault initiates in the bearing, there is an increment in the vibration response. This increment can be quantified by the degradation indicator computed from the vibration signal. The Variational Mode Decomposition technique is used to de-noise the vibration signals. Various statistical features are derived from the de-noised signal, and the best feature subset is chosen by the Recursive Feature Elimination method. Then, the significant bearing life degradation indicator is computed using the Reconstruction Independent Component Analysis method by fusing selected features. A novel index is formulated for computing the percentage of failure, which can identify whether the bearing is in a mild failure state or medium failure state. The efficiency of the proposed framework is demonstrated using experimental bearing datasets.

show abstract

Section: Introductionmentioning

confidence: 99%