Abstract
This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra of vibration signals to detect and diagnose bearing faults. In this paper, a novel vibration-based BFDD via a probability plot (ProbPlot) image recognition technique under constant and variable speed conditions is proposed. The proposed technique is based on the absolute value principal component analysis (AVPCA), namely, ProbPlot via image recognition using the AVPCA (ProbPlot via IR-AVPCA) technique. A comparison of the features (images) obtained: (1) directly in the time domain from the original raw data of the vibration signals; (2) by capturing the Fast Fourier Transformation (FFT) of the vibration signals; or (3) by generating the probability plot (ProbPlot) of the vibration signals as proposed in this paper, is considered. A set of realistic bearing faults (i.e., outer-race fault, inner-race fault, and balls fault) are experimentally considered to evaluate the performance and effectiveness of the proposed ProbPlot via the IR-AVPCA method.
Highlights
Detecting and diagnosing the different faults in modern engineered systems is crucial to designing a better condition monitoring strategy for preventing the degradation from early stage simple faults into serious or even catastrophic system failures
Many possible choices exist for generating the vibration signal features in the form of images to bearing fault detection and diagnosis (BFDD): (i) the features can be obtained directly in time domain from the original raw data of the vibration signals under the different bearing health conditions; (ii) they can be obtained in frequency domain by, for example, capturing the Fourier Transformation (FFT) of the vibration signals as in [23]; and (iii) or they can be obtained by generating the ProbPlot of the vibration signals as proposed for the first time in this paper, which has the advantage of not needing computation or analysis compared, unlike the FFT-based method
The proposed ProbPlot technique is more suitable for online bearing fault detection and diagnosis and for being adapted to real industrial applications, since it does not require any computation or space compared to the use of the FFT of the vibration signals, and since it can detect and diagnose the different bearing faults under nonstationary conditions, unlike the FFT
Summary
Detecting and diagnosing the different faults in modern engineered systems is crucial to designing a better condition monitoring strategy for preventing the degradation from early stage simple faults into serious or even catastrophic system failures. This paper proposed a novel BFDD technique that uses the ProbPlot to generate features in the form of images and an image recognition technique based on the AVPCA [4], namely, the ProbPlot via IR-AVPCA technique, to detect and diagnose the different bearing faults under constant and variable speed conditions. Many possible choices exist for generating the vibration signal features in the form of images to BFDD: (i) the features (images) can be obtained directly in time domain from the original raw data of the vibration signals under the different bearing health conditions; (ii) they can be obtained in frequency domain by, for example, capturing the FFT of the vibration signals as in [23]; and (iii) or they can be obtained by generating the ProbPlot of the vibration signals as proposed for the first time in this paper, which has the advantage of not needing computation or analysis compared, unlike the FFT-based method. A detailed description of the proposed ProbPlot via IR-AVPCA technique to BFDD is given below
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