Feature extraction method of bearing AE signal based on improved FAST-ICA and wavelet packet energy

Long, Han; Li, Cheng Wei; Guo, Song; Su, Xun

doi:10.1016/j.ymssp.2015.03.009

Cited by 44 publications

(17 citation statements)

References 4 publications

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“…In addition, when the vibration signal is decomposed into different frequency bands via the wavelet packet, the frequency band is confined by the energy and frequency. In this paper, the square of the WPD coefficient refers to the energy of the signal in the time domain [23]. From Fig.…”

Section: Feature Intensification Of Vibration Signal In the Drilling mentioning

confidence: 99%

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Zhou¹,

Li²,

Liu³

2019

SV-JME

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To address the difficulty in extracting the features of vibration signals under intense background noise, a new method is proposed based on wavelet packet multi-band spectral subtraction to intensify vibration signal features in drilling processing. First, it is assumed that the spindle vibration signal of machine idling and the vibration signal caused when the tool cuts the workpiece are independent of each other, and the machine's idling signal is perceived as the 'additive noise' of the monitoring signal in light of the spectral subtraction principles. Secondly, in line with the characteristics of vibration signals in the drilling process, the 'additive noise' and monitoring signal are split into multiple frequency bands via wavelet packet decomposition. Eventually, spectral subtraction is performed independently in each band, and the vibration signals are reconstructed. The simulations and experimental results indicate that the new method should effectively eliminate the impact of environmental noise on the process of feature extraction to intensify the features of the monitoring signal. Highlights• A new method is proposed based on wavelet packet multi-band spectral subtraction to intensify vibration signal features in drilling processing. • We have addressed the difficulty in extracting the weak features of vibration signals under intense background noise. • Compared with the traditional threshold de-noising method, the problem of threshold selection can be avoided.

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Section: Feature Intensification Of Vibration Signal In the Drilling mentioning

confidence: 99%

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Zhou¹,

Li²,

Liu³

2019

SV-JME

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“…Among the speech recognition methods, independent component analysis (ICA) shows good performance in processing linear mixtures of signals produced by multiple sources and has been successfully used for the separation of other kinds of signals. Han et al [ 20 ] used the FAST-ICA (an implementation of the fast fixed-point algorithm for ICA) and wavelet packet method to separate the vibration signals of rolling bearings mixed by a set of sources. The problem is that, when using ICA methods, the number of sensors should be more than the number of sources and all of the mixed signals should be synchronized.…”

Section: Introductionmentioning

confidence: 99%

Aliasing Signal Separation of Superimposed Abrasive Debris Based on Degenerate Unmixing Estimation Technique

Wang

Zio

et al. 2018

Sensors

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Leakage is the most important failure mode in aircraft hydraulic systems caused by wear and tear between friction pairs of components. The accurate detection of abrasive debris can reveal the wear condition and predict a system’s lifespan. The radial magnetic field (RMF)-based debris detection method provides an online solution for monitoring the wear condition intuitively, which potentially enables a more accurate diagnosis and prognosis on the aviation hydraulic system’s ongoing failures. To address the serious mixing of pipe abrasive debris, this paper focuses on the superimposed abrasive debris separation of an RMF abrasive sensor based on the degenerate unmixing estimation technique. Through accurately separating and calculating the morphology and amount of the abrasive debris, the RMF-based abrasive sensor can provide the system with wear trend and sizes estimation of the wear particles. A well-designed experiment was conducted and the result shows that the proposed method can effectively separate the mixed debris and give an accurate count of the debris based on RMF abrasive sensor detection.

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“…Other frequently used methods for the reduction of feature dimensions are the independent component analysis (ICA) and the distance evaluation technique (DET). 9 Han et al 10 used ICA-based method to develop a fast algorithm which had a faster convergence speed and higher precision. That algorithm was integrated with the wavelet packet energy spectrum and can accurately recognize the slight damage and fracture of a bearing.…”

Section: Introductionmentioning

confidence: 99%

A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery

You

Shen

Guo

et al. 2017

Advances in Mechanical Engineering

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Rolling element bearings and gears are the most common machine elements. As they are extensively used in rotating machinery, their health conditions are crucial to the safe operation. The signals measured from rotating machines are usually affected by the working conditions and background noises. Thus, identifying faults from the mixed signals is a challenging and important task. Deep learning is initially developed for image recognition. Recently, it has attracted increasing attention in machinery fault diagnosis research. However, the generalization ability of the default classifier of it is not very satisfying. Thus, combining the feature learning ability of deep learning and the existing classifiers with satisfactory generalization ability is necessary. In this article, a hybrid technique based on convolutional neural network and support vector regression is proposed. The former part is used to promote feature extraction capability, and the latter part is used for multi-class classification. The efficiency of the proposed scheme is validated using the real acoustic signals measured from locomotive bearings and vibration signals measured from the automobile transmission gearbox. Results confirm that the method proposed is able to capture fault characteristics from the raw data, and both bearing faults and gear faults can be detected successfully.

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Feature extraction method of bearing AE signal based on improved FAST-ICA and wavelet packet energy

Cited by 44 publications

References 4 publications

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Aliasing Signal Separation of Superimposed Abrasive Debris Based on Degenerate Unmixing Estimation Technique

A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery

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