Time-Varying and Multiresolution Envelope Analysis and Discriminative Feature Analysis for Bearing Fault Diagnosis

Kang, Myeongsu; Kim, Jae‐Young; Wills, Linda M.; Kim, Jong‐Myon

doi:10.1109/tie.2015.2460242

Cited by 124 publications

(70 citation statements)

References 34 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Abstract:The fast Fourier transform (FFT) is the most prevalent algorithm for the spectral analysis of acoustic emission signals acquired at ultra-high sampling rates to monitor the condition of rotary machines. The complexity and cost of the associated hardware limit the use of FFT in real-time applications. In this paper, an efficient hardware architecture for FFT implementation is proposed based on the radix-2 decimation in frequency algorithm (R2DIF) and a feedback pipelined technique (FB) that allows effective sharing of storage between the input and output data at each stage of the FFT process via shift registers. The proposed design uses an optimal hybrid rotation scheme by combining the modified coordinate rotation digital computer (m-CORDIC) algorithm and a binary encoding technique based on canonical signed digit (CSD) for replacing the complex multipliers in FFT. The m-CORDIC algorithm, with an adaptive iterative monitoring process, improves the convergence of computation, whereas the CSD algorithm optimizes the multiplication of constants using a simple shift-add method. Therefore, the proposed design does not require the large memory typically entailed by existing designs to carry out twiddle factor multiplication in large-point FFT implementations, thereby reducing its area on the chip. Moreover, the proposed pipelined FFT processor uses only distributed logic resources and does not require expensive dedicated functional blocks. Experimental results show that the proposed design outperforms existing state-of-the-art approaches in speed by about 49% and in resource utilization by around 51%, while delivering the same accuracy and utilizing less chip area.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

Self Cite

View full text Add to dashboard Cite

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“…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]. One of the main advantages of using envelope analysis is the fact that it is more robust to different sorts of noises that may affect the raw signal of bearings.…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen

Kim

2018

Applied Sciences

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Exact evaluation of the degradation levels in bearing defects is one of the most essential works in bearing condition monitoring. This paper proposed an efficient evaluation method using a deep neural network (DNN) for correct prediction of degradation levels of bearings under different crack size conditions. An envelope technique was first used to capture the characteristic fault frequencies from acoustic emission (AE) signals of bearing defects. Accordingly, a health-related indicator (HI) calculation was performed on the collected envelope power spectrum (EPS) signals using a Gaussian window method to estimate the fault severities of bearings that served as an appropriate dataset for DNN training. The proposed DNN was then trained for effective prediction of bearing degradation using the Adam optimization-based backpropagation algorithm, in which the synaptic weights were optimally initialized by the Xavier initialization method. The effectiveness of the proposed degradation prediction approach was evaluated through different crack size experiments (3, 6, and 12 mm) of bearing faults.

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“…Therefore, the time-domain and frequency-domain analysis approaches cannot have essential effects [9]. For these signals, time-frequency analysis can provide an effective way for features extraction.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

Dong

Ding

et al. 2018

Shock and Vibration

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In order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important. The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal overlap discrete wavelet packet transform (MODWPT). In order to reduce redundancy information of original statistical feature set, features selection by adjusted rand index and sum of within-class mean deviations (FSASD) was proposed to select fault sensitive features. Furthermore, a modified features dimensionality reduction method, supervised neighborhood preserving embedding with label information (SNPEL), was proposed to realize low-dimensional representations for high-dimensional feature space. Finally, vibration signals collected from two experimental test rigs were employed to evaluate the performance of the proposed procedure. The results show that the effectiveness, adaptability, and superiority of the proposed procedure can serve as an intelligent bearing fault diagnosis system.

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Time-Varying and Multiresolution Envelope Analysis and Discriminative Feature Analysis for Bearing Fault Diagnosis

Cited by 124 publications

References 34 publications

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

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

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

Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

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