Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds

Abstract

Early identification of failures in rolling element bearings is an important research issue in mechanical systems. In this study, a reliable methodology for bearing fault detection is proposed, which is based on an optimal sub-band selection scheme using the discrete wavelet packet transform (DWPT) and envelope power analysis techniques. A DWPT-based decomposition is first performed to extract the characteristic defect features from the acquired acoustic emission (AE) signals. The envelope power spectrum (EPS) of each sub-band signal is then calculated to detect the characteristic defect frequencies to reveal abnormal symptoms in bearings. The selection of an appropriate sub-band is essential for effective fault diagnosis, as it can reveal intrinsically explicit information about different types of bearing faults. To address this issue, we propose a Gaussian distribution model-based health-related index (HI) that is a powerful quantitative parameter to accurately estimate the severity of bearing defects. The most optimal sub-band for fault detection is determined using two dimensional (2D) visualization analysis. The efficiency of the proposed approach is validated using several experiments in which different defect conditions are identified under variable, and low operational speeds.

Effective Prediction of Bearing Fault Degradation under Different Crack Sizes Using a Deep Neural Network

“…Through the observation of the abnormal symptoms at the harmonic frequencies of BPFO, BPFI, and 2 × BSF, an optimal approach for evaluating the defect severity of bearings was applied to correctly measure the defective degree, which was computed by using the GDM-based window method around harmonics of the fault frequencies, respectively. This GDM-based method has been extensively used for HI calculation to estimate the defect severity of bearings [8] and results in detecting and diagnosing the bearing defects. Figure 3 describes the process of estimating fault severity based on the Gaussian window method.…”

“…Some outstanding signal processing technologies have been extensively used to extract the characteristic fault frequencies from obtained bearing data for an effective incipient fault detection in critical mechanical systems [5][6][7][8]. The envelope analysis method is an effective technique to extract the low-frequency fault components from the high-frequency carrier signal recording bearing conditions [4,6].…”

A Comparative Study of Four Kinds of Adaptive Decomposition Algorithms and Their Applications

“…It is difficult to find a rigorous definition of the adaptive decomposition algorithm; however, we think that such a type of method can form a series of sparse representations in the decomposition process, which is different with “rigid” methods, such as the Fourier or wavelets transforms, corresponding to the use of some basis (or frame) designed independently of the processed signal [ 1 , 2 ]. As many kinds of signals in engineering problems are non-linear and non-stationary, such as fault signals of mechanical equipment [ 3 , 4 , 5 , 6 , 7 , 8 ], some modal test signals [ 9 ], acoustic signals of non-destructive testing [ 10 , 11 ] and condition monitoring signals for rail track [ 12 , 13 , 14 ], the adaptive decomposition algorithm has superiority for analyzing these signals, because of decomposition flexibility.…”

A Pipelined FFT Processor Using an Optimal Hybrid Rotation Scheme for Complex Multiplication: Design, FPGA Implementation and Analysis

“…State-of-the-art methods for the intelligent maintenance of rotary machines rely on the timely and accurate analysis of condition monitoring signals, such as acoustic emissions (AE) [1][2][3][4] and vibration acceleration signals [5,6]. AE signals are sampled at very high frequencies, typically 1 MHz, to capture ultrasonic sounds released during the initiation and propagation of cracks in machine components.…”

“…Ever since its introduction, the computational advantages of the FFT have made it an essential algorithm with widespread applications in science and engineering, such as communication, signal processing, image processing, bio-robotics, and intelligent maintenance [1,2,4,[7][8][9][10]. The high-speed requirements of smart maintenance systems, such as fault diagnosis in rotary machines using the spectral analysis of AE signals, necessitate a high-performance FFT processor.…”