Design of mixture de-noising for detecting faulty bearing signals

Shao, Yimin; Nezu, Kikuo

doi:10.1016/j.jsv.2004.03.051

Cited by 43 publications

(23 citation statements)

References 12 publications

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“…Previous application of adaptive filter (AF) in condition monitoring origins from classical linear prediction problem [3,4,[6][7][8][9]16,20]. It is well known that for autoregressive process with discrete time, the next sample of signal at any t time may be estimated using previous P samples (see Fig.…”

Section: Schur Filter Vs Lms Filter-using Prediction Error Signals Fomentioning

confidence: 99%

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A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings

Makowski

Zimroz

2013

Mechanical Systems and Signal Processing

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Section: Schur Filter Vs Lms Filter-using Prediction Error Signals Fomentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9]. Typical damages in the REB produce cyclic disturbance in the signal [1,2,5,7,8,10,35], however, usually they are completely masked by the noise [3,4,6,20,36] or other faults [31]. Moreover impulses may have amplitudes that vary in time due to time varying transmission path, load and properties of noise [7].…”

Section: Introductionmentioning

confidence: 99%

A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings

Makowski

Zimroz

2013

Mechanical Systems and Signal Processing

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“…In every case good results were obtained. The noise ratio was calculated according to equation (7) as defined in [26]: (7) where z n is the Gaussian noise signal, N is the number of samples, and y n is the noisy signal. The signal ratio used in this simulation is equal to 55%; that means an SNR of 1.44.…”

Section: (6)mentioning

confidence: 99%

Application of the Wavelet Multi-resolution Analysis and Hilbert transform for the prediction of gear tooth defects

et al. 2012

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Publication informationMeccanica, 47 (7) Abstract -In machine defect detection, namely those of gears, the major problem is isolating the defect signature from the measured signal, especially where there is significant background noise or multiple machine components. This article presents a method of gear defect detection based on the combination of Wavelet Multi-resolution Analysis and the Hilbert transform. The pairing of these techniques allows simultaneous filtering and denoising, along with the possibility of detecting transitory phenomena, as well as a demodulation. This paper presents a numerical simulation of the requisite mathematical model followed by its experimental application of acceleration signals measured on defective gears on a laboratory test rig. Signals were collected under various gear operating conditions, including defect size, rotational speed, and frequency bandwidth. The proposed method compares favourably to commonly used analysis tools, with the advantage of enabling defect frequency isolation, thereby allowing detection of even small or combined defects.

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“…This can easily excite multimodel resonance responses of the structure and sensor [9]. The impulses generated by bearing faults are usually masked by background noises and structural vibration [10,11]. In addition, the impulses generated by bearing faults are mixed with those caused by the wheelrail interaction when the wheel tread has defects or is out of roundness.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold

Zhuang

Ding

Tan

et al. 2017

Shock and Vibration

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A novel fault detection method employing the impulse-envelope manifold is proposed in this paper which is based on the combination of convolution sparse representation (CSR) and Hilbert transform manifold learning. The impulses with different sparse characteristics are extracted by the CSR with different penalty parameters. The impulse-envelope space is constructed through Hilbert transform on the extracted impulses. The manifold based on impulse-envelope space (impulse-envelope manifold) is executed to learn the low-dimensionality intrinsic envelope of vibration signals for fault detection. The analyzed results based on simulations, experimental tests, and practical applications show that (1) the impulse-envelope manifold with both isometric mapping (Isomap) and locally linear coordination (LLC) can be successfully used to extract the intrinsic envelope of the impulses where local tangent space analysis (LTSA) fails to perform and (2) the impulse-envelope manifold with Isomap outperforms those with LLC in terms of strengthening envelopes and the number of extracted harmonics. The proposed impulse-envelope manifold with Isomap is superior in extracting the intrinsic envelope, strengthening the amplitude of intrinsic envelope spectra, and enlarging the harmonic number of fault-characteristic frequency. The proposed technique is highly suitable for extracting intrinsic envelopes for bearing fault detection.

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Design of mixture de-noising for detecting faulty bearing signals

Cited by 43 publications

References 12 publications

A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings

A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings

Application of the Wavelet Multi-resolution Analysis and Hilbert transform for the prediction of gear tooth defects

Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold

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