Bearing Fault Diagnosis Using Empirical Mode Decomposition, Entropy Based Features And Data Mining Techniques. (2018)

• Record Type: Journal Article
• Authors: reddy, M. sai pradeep; reddy, D. Mallikarjiuna; Devendiran, S.; Mathew, Arun Tom
• Is Part Of: Materials today. Volume 5:Number 5(2018)Part 2
• Page Start: 11460
• Page End: 11475
• Publication Date: 2018
• Subjects: Tapered roller bearing -- Fault diagnosis -- Bearing setup -- Empirical mode decomposition -- Entropy-based features -- Classification algorithms; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2018.02.114
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Rolling bearing is an important part of rotating machinery. Working condition of bearing will directly affect the operation and function of equipment. Therefore, the testing and fault diagnosis of the running state of bearing is significant. A common method for extracting the mechanical fault is assuming that vibration signals are stable. But the real measured signals often are non- stationary. For this nonlinear phenomenon, it is difficult to solve the problem fundamentally by the traditional power spectrum analysis .In this present work aims to formulate an automated prediction model using vibration signals of various bearing conditions by using EMD (empirical mode decomposition) and entropy based features and different classification algorithms.