Discrimination of Multiple Faults in Bearings Using Density-Based Orthogonal Functions of the Time Response

Abstract

This study investigates the use of the mapped density of time response using orthogonal functions to detect single and multiple faults in rolling element bearings. The method is based on constructing the density of a single time response of the system by using orthogonal functions. The coefficients of the orthogonal functions create the feature vector in order to discriminate between different rolling element bearing faults. The method does not require preprocessing of the data, noise reduction, or feature selection. This method has been applied to vibration data of different bearing conditions at rotational speeds ranging from 300 rpm to 3000 rpm. These conditions include a healthy bearing, and bearings with defects in inner race, outer race, combination of inner race and outer race and rolling element. The results have shown remarkable detection efficiency in the case of a single and two bearing fault configurations. In general, for all bearing configurations, the approach has high performance in detecting defective conditions. These results indicate that using the mapped density to characterize the system under different conditions has considerable potential in bearing diagnostics.

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

“…An overall accuracy of 97.0% was achieved using this procedure. In [16,30], we introduced the mapped density method to discriminate simultaneous bearing faults under various rotational speeds. In this work, we studied the use of the information provided by proximity probe sensors.…”

Multi-speed Multi-load Bearing Diagnostics Using Extended Phase Space Topology

“…The coefficients of the orthogonal polynomials are then used as features. Our previous work [7][8][9] explains in detail the derivation and the implementation of the EPST method. A linear support vector machine (SVM) with optimal parameters was built in order identify the bearing condition i.e., H, B, IR and OR using features of the EPST method.…”

“…Our previous work [4][5][6] introduced and developed a family of methods based on the phase space characterization for different dynamical systems. In the present paper, we apply a novel feature extraction technique that we call Extended Phase Space Topology (EPST) [7][8][9] in order to detect and identify bearings with different health statuses under multiple motor operating conditions of load and speed.…”

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

“…In [14,15], we introduced the mapped density method in order to discriminate simultaneous bearing faults under various rotational speeds. In this work we studied the use of the information provided by proximity probe sensors.…”