Fault Diagnosis of Ball Bearing Elements: A Generic Procedure based on Time-Frequency Analysis

Abstract

Motor-driven machines, such as water pumps, air compressors, and fans, are prone to fatigue failures after long operating hours, resulting in catastrophic breakdown. The failures are preceded by faults under which the machines continue to function, but with low efficiency. Most failures that occur frequently in the motor-driven machines are caused by rolling bearing faults, which could be detected by the noise and vibrations during operation. The incipient faults, however, are difficult to identify because of their low signal-to-noise ratio, vulnerability to external disturbances, and non-stationarity. The conventional Fourier spectrum is insufficient for analyzing the transient and non-stationary signals generated by these faults, and hence a novel approach based on wavelet packet decomposition and support vector machine is proposed to distinguish between various types of bearing faults. By using wavelet and statistical methods to extract the features of bearing faults based on time-frequency analysis, the proposed fault diagnosis procedure could identify ball bearing faults successfully.

Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment

“…N B is the number of balls, is the pitch diameter, is the ball diameter, is the contact angle, and is the rotation speed. The 6204-T1 bearing was used in this experiment, the parameters and dimensions of the roller bearing are described in [19]. The vibration signal of each experiment was collected and labeled for each bearing condition.…”

“…Furthermore, the vibration signal contaminated by signal noise could be filtered by using the wavelet packet decomposition (WPD), the energy of the WPD coefficients could be utilized to detect the rolling bearing failures effectively [18]. The WPD and statistical methods were developed to successfully extract the significant features of bearing faults using time-frequency analysis [19].…”

Signal Processing and Analysis of Electrical Circuit

“…The proposed topics are essential for industry. Signal processing and analysis of diagnostic signals are used for fault diagnosis and monitoring systems [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Signal processing and image processing methods are used for many applications, for example medical applications [27][28][29][30][31][32][33][34][35][36].…”

A New Demodulation Method for Mechanical Fault Feature Extraction based on LOD and IEE

“…Therefore, to obtain the fault feature information, the original signal must be decomposed into mono-component signal first, and then demodulated [1], [2]. At present, the commonly used signal decomposition methods include wavelet transform (WT) [3], empirical mode decomposition (EMD) [4], local mean decomposition (LMD) [5], and so on. They have been widely used in fault signal decomposition [6]- [10].…”