Rolling Element Bearing Diagnostics Using Extended Phase Space Topology

Abstract

This paper introduces a novel method called extended phase space topology (EPST) for machinery diagnostics and pattern recognition. In particular, the research focuses on fault detection and diagnostics of rolling element bearings. The proposed method is based on mapping the vibrational response onto the density space and approximating the density using orthogonal functions. The method has been applied to vibration data of a rotating machine where the data were measured by proximity probes. The method was applied to two operating conditions: constant operating speed and variable operating speed. As will be shown, the proposed feature extraction method has an outstanding capability in characterizing the system response and diagnosing the system. The method is evidently robust to noise, does not depend on expert knowledge about the system, requires no feature ranking or selection, and can easily be applied in an automated process. Finally, a comparison with utilization of statistical features is performed for each operating condition, which demonstrates that the proposed method performs better than the traditional statistical methods.

A Review of Phase Space Topology Methods for Vibration-Based Fault Diagnostics in Nonlinear Systems

“…In [13][14][15]31], the PST method was introduced. In this work, we performed bearing diagnostics on different rotational speeds domains and obtained very good results (overall accuracy of 96.7% ).…”

“…Also, outstanding performance was achieved using RQA parameters to identify various gear conditions with 100% accuracy, 100% recall and 100% precision in detecting multiple cracks and missing tooth conditions. In [18], the PST method was applied to detect anomalous behavior and to diagnose various gear defects. Results indicates 99% accuracy in classifying between different gear conditions.…”

An overview of PST for vibration based fault diagnostics in rotating machinery

“…In [4,16,17], the EPST method was introduced. In this work we perform bearing diagnostics on different rotational speeds domains and obtained very good results (overall accuracy of 96.7%).…”

“…The theoretical approaches are validated using data from fault simulators at Villanova University and Case Western University; we also validate our algorithms using experimental data from practical machinery provided by United Technologies Research Center (UTRC, USA) and Federal University of Uberlândia (Brazil). [4] The rest of the paper is organized as follows. Section 2 describes a family of methods that were originated and derived by our team: Phase Space Topology (PST) and Extended Phase Space Topology (EPST).…”

Gear Fault Detection Using Recurrence Quantification Analysis and Support Vector Machine

“…However, the the problem of multiple fault detection was not addressed. The Extended Phase Space Topology (EPST) based on characterizing the phase space topology, developed in our research laboratory, was applied on the same system used in this study [26] [27] [28] [29]. The results indicate that EPST is efficient in diagnosing the status of the health of the gear system and characterizing the dynamic behavior.…”