A new fault feature for rolling bearing fault diagnosis under varying speed conditions

Ren, Yong; Li, Wenyuan; Zhu, Zhencai; Tong, Zhe; Zhou, Gongbo

doi:10.1177/1687814017703897

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…, P f is the fitness of particle, and the size of the fitness corresponds to the distance between each bird and food. The extremum of individual P b and extremum of population g b can be updated according to particle fitness, and then we can use the individual extremum and the population extremum to calculate the particle velocity and position, as shown in equations (27) and (28)…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…PSO is used to optimize the parameters in the DNN. If the DNN contains a total of k parameters, the dimensional space j in equations (27) and (28) is equal to k. The number of particles is set empirically, and each particle contains j parameters. Select the best performing particle after t iterations and attach its j parameters to the DNN as the initial parameters.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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In recent years, research on gear pitting fault diagnosis has been conducted. Most of the research has focused on feature extraction and feature selection process, and diagnostic models are only suitable for one working condition. To diagnose early gear pitting faults under multiple working conditions, this article proposes to develop a domain adaptation diagnostic model–based improved deep neural network and transfer learning with raw vibration signals. A particle swarm optimization algorithm and L2 regularization are used to optimize the improved deep neural network to improve the stability and accuracy of the diagnosis. When using the domain adaptation diagnostic model for fault diagnosis, it is necessary to discriminate whether the target domain (test data) is the same as the source domain (training data). If the target domain and the source domain are consistent, the trained improved deep neural network can be used directly for diagnosis. Otherwise, the transfer learning is combined with improved deep neural network to develop a deep transfer learning network to improve the domain adaptability of the diagnostic model. Vibration signals for seven gear types with early pitting faults under 25 working conditions collected from a gear test rig are used to validate the proposed method. It is confirmed by the validation results that the developed domain adaptation diagnostic model has a significant improvement in the adaptability of multiple working conditions.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…FFT has very wide scientific and technological application that includes: image analysis, EEG signal processing, optics, investment portfolio risk management, and engineering applications that include the detection of local malfunction in rotating machines. 23–25 Other commonly seen time-frequency analysis applications include short-time Fourier transform, 26,27 Wigner–Ville distribution, 28 and wavelet transform. 29 Time-frequency analysis is used to extract the features, after which the malfunction category, cause, and location must be identified.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of ball-bearing faults using support vector machine based on the artificial fish-swarm algorithm

Lin

Chu

Wang

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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Ball bearings are important parts of all modern rotating machines. Their function is to reduce friction, support rotating shafts and spindles, and bear loads. Bearing damage can result in abnormal vibrations, cause machine malfunction, and even be dangerous. In this study, analysis of four different ball-bearing conditions was carried out: normal bearings and bearings with inner ring, rolling body, and outer ring malfunction. This was based on electromechanical vibration signals produced on a fault diagnosis simulation platform. The objective was to use a series of signal processing analytical methods to build a set of identification models used to forecast malfunction. Wavelet packet transform technology was first used to process the vibration signal. The signals were pre-processed and analyzed before eigenvalue calculation was done to analyze the signal changes which allowed determination of the nature of the bearing malfunction to be made. The extracted eigenvalues and ball-bearing status categories were input to the support vector machine for model training and testing. Finally, the constructed model parameters were integrated with particle swarm optimization, and the artificial fish-swarm algorithm was used to obtain the optimal parameters for the classifier, and this improved the accuracy of malfunction classification.

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“…In practice, rolling bearing often works under variable speed conditions when the vibration signal shows strong nonstationary characteristics, which increases difficulty of fault diagnosis. In the field of rolling bearing fault diagnosis under variable speed [6,7], order tracking is one of the most effective method [8]. ere are a lot of ways for order tracking.…”

Section: Introductionmentioning

confidence: 99%

An Improved Time‐Frequency Analysis Method for Instantaneous Frequency Estimation of Rolling Bearing

Ruan

Chen

et al. 2018

Shock and Vibration

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Instantaneous frequency estimation of rolling bearing is a key step in order tracking without tachometers, and time-frequency analysis method is an effective solution. In this paper, a new method applying the variational mode decomposition (VMD) in association with the synchroextracting transform (SET), named VMD-SET, is proposed as an improved time-frequency analysis method for instantaneous frequency estimation of rolling bearing. The SET is a new time-frequency analysis method which belongs to a postprocessing procedure of the short-time Fourier transform (STFT) and has excellent performance in energy concentration. Considering nonstationary broadband fault vibration signals of rolling bearing under variable speed conditions, the time-frequency characteristics cannot be obtained accurately by SET alone. Thus, VMD-SET method is proposed. Firstly, the signal is decomposed into several intrinsic mode functions (IMFs) with different center frequency by VMD. Then, effective IMFs are selected by mutual information and kurtosis criteria and are reconstructed. Next, the SET method is applied to the reconstructed signal to generate the time-frequency representation with high resolution. Finally, instantaneous frequency trajectory can be accurately extracted by peak search from the time-frequency representation. The proposed method is free from time-varying sidebands and is robust to noise interference. It is proved by numerical simulated signal analysis and is further validated by lab experimental rolling bearing vibration signal analysis. The results show this method can estimate the instantaneous frequency with high precision without noise interference.

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A new fault feature for rolling bearing fault diagnosis under varying speed conditions

Cited by 7 publications

References 30 publications

A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

Diagnosis of ball-bearing faults using support vector machine based on the artificial fish-swarm algorithm

An Improved Time‐Frequency Analysis Method for Instantaneous Frequency Estimation of Rolling Bearing

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