Incrementally accumulated holographic SDP characteristic fusion method in ship propulsion shafting bearing fault diagnosis

Abstract To improve the accuracy of fault diagnosis of ship propulsion shaft bearing in a harsh working environment, a visual diagnosis method based on incrementally accumulated holographic symmetrical dot pattern (SDP) characteristic fusion method is proposed in this research. The current study simultaneously extracts the time- and frequency-domain characteristic parameters of vibration signal based on the incremental accumulation method to avoid inconspicuous difference and small discrimination generated by a single parameter. Subsequently, the extracted characteristic signals are transformed into a 2D image based on the SDP method to enhance the differences between signals. Eventually, bearing fault is diagnosed based on the similarity recognition method. Simulation and engineering experiments were conducted to verify the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively diagnose the ship propulsion shaft bearing fault diagnosis.

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Auto Gearbox Bearing Fault Diagnosis Method Based on Winger-SVD

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.1123 ◽

2014 ◽

Vol 543-547 ◽

pp. 1123-1127 ◽

Cited By ~ 1

Author(s):

Shui Yong Yang ◽

Hong Mao Qin

Keyword(s):

Fault Diagnosis ◽

Vibration Signal ◽

Singular Values ◽

Bearing Fault ◽

Feature Vectors ◽

Bearing Fault Diagnosis ◽

Diagnosis Method ◽

Bearing Vibration ◽

Signature Sequence

A new auto gearbox bearing fault diagnosis method is put forward based on Winger distribution and SVD of vibration signal. Firstly, bearing vibration signal was analyzed by Winger distribution; then, signature sequence was obtained by SVD, which can reflect the gearbox fault condition; finally, singular values of the vibration signal Winger spectrums were selected as feature vectors to diagnose fault based SVM. Experiments show that this method can extract fault feature effectively.

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A bearing fault diagnosis method based on the low-dimensional compressed vibration signal

Advances in Mechanical Engineering ◽

10.1177/1687814015593442 ◽

2015 ◽

Vol 7 (7) ◽

pp. 168781401559344 ◽

Cited By ~ 26

Author(s):

Xinpeng Zhang ◽

Niaoqing Hu ◽

Lei Hu ◽

Ling Chen ◽

Zhe Cheng

Keyword(s):

Fault Diagnosis ◽

Vibration Signal ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method ◽

Low Dimensional

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A Bearing Fault Diagnosis Method Using Multi-Branch Deep Neural Network

Machines ◽

10.3390/machines9120345 ◽

2021 ◽

Vol 9 (12) ◽

pp. 345

Author(s):

Van-Cuong Nguyen ◽

Duy-Tang Hoang ◽

Xuan-Toa Tran ◽

Mien Van ◽

Hee-Jun Kang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Fault Diagnosis ◽

Deep Neural Network ◽

Image Representation ◽

Vibration Signal ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method ◽

Multiple Domain

Feature extraction from a signal is the most important step in signal-based fault diagnosis. Deep learning or deep neural network (DNN) is an effective method to extract features from signals. In this paper, a novel vibration signal-based bearing fault diagnosis method using DNN is proposed. First, the measured vibration signals are transformed into a new data form called multiple-domain image-representation. By this transformation, the task of signal-based fault diagnosis is transferred into the task of image classification. After that, a DNN with a multi-branch structure is proposed to handle the multiple-domain image representation data. The multi-branch structure of the proposed DNN helps to extract features in multiple domains simultaneously, and to lead to better feature extraction. Better feature extraction leads to a better performance of fault diagnosis. The effectiveness of the proposed method was verified via the experiments conducted with actual bearing fault signals and its comparisons with well-established published methods.

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Curvature enhanced bearing fault diagnosis method using 2D vibration signal

Journal of Mechanical Science and Technology ◽

10.1007/s12206-020-0501-0 ◽

2020 ◽

Vol 34 (6) ◽

pp. 2257-2266 ◽

Cited By ~ 2

Author(s):

Weifang Sun ◽

Xincheng Cao

Keyword(s):

Fault Diagnosis ◽

Vibration Signal ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method

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A Bearing Fault Diagnosis Method Based on PAVME and MEDE

Entropy ◽

10.3390/e23111402 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1402

Author(s):

Xiaoan Yan ◽

Yadong Xu ◽

Daoming She ◽

Wan Zhang

Keyword(s):

Fault Diagnosis ◽

Vibration Signal ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Fault Features ◽

Whale Optimization ◽

Diagnosis Method ◽

Bearing Vibration ◽

Mode Extraction ◽

Parameter Adaptive

When rolling bearings have a local fault, the real bearing vibration signal related to the local fault is characterized by the properties of nonlinear and nonstationary. To extract the useful fault features from the collected nonlinear and nonstationary bearing vibration signals and improve diagnostic accuracy, this paper proposes a new bearing fault diagnosis method based on parameter adaptive variational mode extraction (PAVME) and multiscale envelope dispersion entropy (MEDE). Firstly, a new method hailed as parameter adaptive variational mode extraction (PAVME) is presented to process the collected original bearing vibration signal and obtain the frequency components related to bearing faults, where its two important parameters (i.e., the penalty factor and mode center-frequency) are automatically determined by whale optimization algorithm. Subsequently, based on the processed bearing vibration signal, an effective complexity evaluation approach named multiscale envelope dispersion entropy (MEDE) is calculated for conducting bearing fault feature extraction. Finally, the extracted fault features are fed into the k-nearest neighbor (KNN) to automatically identify different health conditions of rolling bearing. Case studies and contrastive analysis are performed to validate the effectiveness and superiority of the proposed method. Experimental results show that the proposed method can not only effectively extract bearing fault features, but also obtain a high identification accuracy for bearing fault patterns under single or variable speed.

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Bearing Fault Diagnosis Method Based on EEMD and LSTM

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2020.1.3780 ◽

2020 ◽

Vol 15 (1) ◽

Cited By ~ 2

Author(s):

Ping Zou ◽

Baocun Hou ◽

Jiang Lei ◽

Zhenji Zhang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Small Sample Size ◽

Vibration Signal ◽

Rolling Bearing ◽

Small Sample ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Signal Preprocessing ◽

Diagnosis Method

The condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system.In the past few years, deep neural network (DNN) has been recognized as an effective tool to detect rolling bearing faults. However, It is too complex to directly feed the original vibration signal to the DNN neural network, and the accuracy of fault identification is not high. By using the signal preprocessing technology, the original signal can be effectively removed and preprocessed without losing the key diagnosis information. In this paper, a new EEMD-LSTM bearing fault diagnosis method is proposed, which combines the signal preprocessing technology with the EEMD method that can get clear fault feature signals, and LSTM technology to extract fault features automatically that improves the efficiency of fault feature extraction. In the case of small sample size, this method can significantly improve the accuracy of fault diagnosis.

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