Self-adaptive bearing fault diagnosis based on permutation entropy and manifold-based dynamic time warping

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

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Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

Shock and Vibration ◽

10.1155/2018/1063645 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Fan Jiang ◽

Zhencai Zhu ◽

Wei Li ◽

Bo Wu ◽

Zhe Tong ◽

...

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Optimal Number ◽

Permutation Entropy ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

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Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406214550014 ◽

2014 ◽

Vol 229 (10) ◽

pp. 1887-1903 ◽

Cited By ~ 11

Author(s):

Ying Zhang ◽

Hongfu Zuo ◽

Fang Bai

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Hankel Matrix ◽

Rolling Bearing ◽

Difference Spectrum ◽

Permutation Entropy ◽

Feature Extraction Method ◽

Bearing Fault ◽

Quantitative Indicator ◽

Bearing Fault Diagnosis

There are mainly two problems with the current feature extraction methods used in the electrostatic monitoring of rolling bearings, which affect their abilities to identify early faults: (1) since noises are mixed in the electrostatic signals, it is difficult to extract weak early fault features; (2) traditional time and frequency domain features have limited ability to provide a quantitative indicator of degradation state. With regard to these two problems, a new feature extraction method for rolling bearing fault diagnosis by electrostatic monitoring sensors is proposed in this paper. First, the spectrum interpolation is adopted to suppress the power-frequency interference in the electrostatic signal. Then the resultant signal is used to construct Hankel matrix, the number of useful components is automatically selected based on the difference spectrum of singular values, after that the signal is reconstructed to remove background noises and random pulses. Finally, the permutation entropy of the denoised signal is calculated and smoothed using the exponential weighted moving average method, which is used to be a quantitative indicator of bearing performance state. The simulation and experimental results show that the proposed method can effectively remove noises and significantly bring forward the time when early faults are detected.

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Permutation entropy-based 2D feature extraction for bearing fault diagnosis

Nonlinear Dynamics ◽

10.1007/s11071-020-06014-6 ◽

2020 ◽

Vol 102 (3) ◽

pp. 1717-1731

Author(s):

Mantas Landauskas ◽

Maosen Cao ◽

Minvydas Ragulskis

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Permutation Entropy ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Rolling bearing fault diagnosis based on intelligent optimized self-adaptive deep belief network

Measurement Science and Technology ◽

10.1088/1361-6501/ab50f0 ◽

2020 ◽

Vol 31 (5) ◽

pp. 055009 ◽

Cited By ~ 1

Author(s):

Shuzhi Gao ◽

Lintao Xu ◽

Yimin Zhang ◽

Zhiming Pei

Keyword(s):

Fault Diagnosis ◽

Rolling Bearing ◽

Deep Belief Network ◽

Belief Network ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Self Adaptive

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Time-Domain Fault Diagnosis Method of Mechanical and Electrical Equipment Based Improved Dynamic Time Wraping

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.693.1539 ◽

2016 ◽

Vol 693 ◽

pp. 1539-1544 ◽

Cited By ~ 1

Author(s):

Zhi Wu Shang ◽

Zhen Wu Liu ◽

Ya Feng Li ◽

Tai Yong Wang

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Time Domain ◽

Dynamic Time Warping ◽

Statistical Parameter ◽

Electrical Equipment ◽

Time Warping ◽

Trend Prediction ◽

Signal Characteristics ◽

Dynamic Time

Dynamic time warping used in speech recognition widely was migrated to fault feature extraction and diagnosis in time domain. Integration of phase compensation, slope weighted, derivative, sliding window connection, fast dynamic time planning method is applied to dynamic time warping method. And a new method of time-domain signal feature extraction and fault diagnostic based on improved dynamic time warping method of mechanical and electrical equipment was proposed. Identification and localization of fault signal characteristics may be done by improving dynamic time warping method to obtain a residual signal sequences with fault characterized sidebands and selecting the statistical characteristic parameters such as peak, RMS, kurtosis spectrum to complete identification and localization of fault signal characteristics. New time-domain fault trend prediction method of mechanical and electrical equipment was established based on new statistical parameter Thikat. A new idea and target was provided for fault diagnosis of mechanical and electrical equipment.

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