Railway bearing and cardan shaft fault diagnosis via an improved morphological filter

Railway faults are usually observed as impulses in the vibration signal, but they are mostly immersed in noise. To effectively remove noise and identify the impulses, an improved morphological filter is proposed in this article. The proposal focuses on two aspects: a novel gradient convolution operator is proposed for feature extraction, and a new fault sensitivity measurement algorithm is proposed for scale selection because a morphological filter’s effectiveness is mainly determined by these two elements. The performance of the improved morphological filter is evaluated with real vibration signals measured from train’s axle bearings and cardan shafts. From the analysis of three sets of railway faults, the results indicate that the proposed morphological filter effectively detects the faults. Compared with three reported morphological filters, the proposed method has better diagnosis effectiveness.

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Application of EEMD Energy Distribution and Grey Incidence in Gearbox Fault Diagnosis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.430 ◽

2013 ◽

Vol 347-350 ◽

pp. 430-433

Author(s):

Wen Bin Zhang ◽

Jia Xing Zhu ◽

Ya Song Pu ◽

Yan Jie Zhou

Keyword(s):

Fault Diagnosis ◽

Energy Distribution ◽

Rank Order ◽

Vibration Signal ◽

Ensemble Empirical Mode Decomposition ◽

Small Sample ◽

Morphological Filter ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Gearbox Vibration

In this paper, a new comprehensive gearbox fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD) and grey incidence. Firstly, the rank-order morphological filter was defined and the line structure element was selected for rank-order morphological filter to de-noise the original acceleration vibration signal. Secondly, de-noised gearbox vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some IMFs containing the most dominant fault information were calculated the energy distribution. Finally, due to the grey incidence has good classify capacity for small sample pattern identification; these energy distributions could serve as the feature vectors, the grey incidence of different gearbox vibration signals was calculated to identify the fault pattern and condition. Practical results show that the proposed method can be used in gear fault diagnosis effectively.

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A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Entropy ◽

10.3390/e23101319 ◽

2021 ◽

Vol 23 (10) ◽

pp. 1319

Author(s):

Haikun Shang ◽

Junyan Xu ◽

Yucai Li ◽

Wei Lin ◽

Jinjuan Wang

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Extraction Method ◽

Vibration Signal ◽

Practical Significance ◽

Stable Operation ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Feature Extraction Method ◽

Multi Scale

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

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Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis

Advances in Mechanical Engineering ◽

10.1177/1687814016683085 ◽

2016 ◽

Vol 8 (12) ◽

pp. 168781401668308 ◽

Cited By ~ 1

Author(s):

Shuangyuan Wang ◽

Yixiang Huang ◽

Liang Gong ◽

Lin Li ◽

Chengliang Liu

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Sparse Coding ◽

Extraction Procedure ◽

Vibration Signal ◽

Support Vector ◽

Data Set ◽

Vibration Signals ◽

Efficiency And Effectiveness ◽

Gear Fault

Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.

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A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

Applied Sciences ◽

10.3390/app11114996 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4996

Author(s):

Gang Yao ◽

Yunce Wang ◽

Mohamed Benbouzid ◽

Mourad Ait-Ahmed

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Vibration Signal ◽

Optimization Techniques ◽

Fault Classification ◽

Vibration Signals ◽

Kernel Parameter ◽

Diagnosis Method ◽

The Impact ◽

Fault Feature Extraction

In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method.

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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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Engine Fault Detection Approach Based on Angle Domain Signal Model

Journal of Physics Conference Series ◽

10.1088/1742-6596/2068/1/012034 ◽

2021 ◽

Vol 2068 (1) ◽

pp. 012034

Author(s):

Hai Zeng ◽

Ning Zeng ◽

Jin Han ◽

Yan Ding

Keyword(s):

Fault Diagnosis ◽

Fault Detection ◽

Failure Detection ◽

Vibration Signal ◽

Bench Test ◽

Signal Model ◽

Vibration Signals ◽

Engine Vibration ◽

Detection Approach ◽

Diagnosis Approach

Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.

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Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

Sensors ◽

10.3390/s21227467 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7467

Author(s):

Shih-Lin Lin

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Fault Diagnosis ◽

Vibration Signal ◽

Image Feature ◽

Signal Acquisition ◽

Time Varying ◽

Intelligent Fault Diagnosis ◽

Image Block ◽

Wide Range

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.

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Mine Ventilator Fault Diagnosis Based on Harmonic Wavelet Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.143-144.613 ◽

2011 ◽

Vol 143-144 ◽

pp. 613-617

Author(s):

Shuang Xi Jing ◽

Yong Chang ◽

Jun Fa Leng

Keyword(s):

Fault Diagnosis ◽

Wavelet Analysis ◽

Frequency Domain ◽

Vibration Signal ◽

Wavelet Function ◽

Frequency Region ◽

Analysis Method ◽

Vibration Signals ◽

Harmonic Wavelet ◽

Frequency Components

Harmonic wavelet function, with the strict box-shaped characteristic of spectrum, has strong ability of identifying signal in frequency domain, and can extract weak components form vibration signals in frequency domain. Using harmonic wavelet analysis method, the selected frequency region and other frequency components of vibration signal of mine ventilator were decomposed into independent frequency bands without any over-lapping or leaking. Simulation and diagnosis example show that this method has good fault diagnosis effect, and the ventilator fault is diagnosed successfully.

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A fractal-dimension-based envelope demodulation for rolling element bearing fault feature extraction from vibration signals

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

10.1177/0954406215608894 ◽

2016 ◽

Vol 230 (18) ◽

pp. 3194-3211 ◽

Cited By ~ 3

Author(s):

Juanjuan Shi ◽

Ming Liang

Keyword(s):

Feature Extraction ◽

Fractal Dimension ◽

Search Algorithm ◽

Vibration Signal ◽

Rolling Element Bearing ◽

Vibration Signals ◽

Bearing Fault ◽

Bearing Condition Monitoring ◽

Interference Frequency ◽

Fault Feature Extraction

Vibration analysis has been extensively used as an effective tool for bearing condition monitoring. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault feature (i.e. fault-induced impulses), periodic interferences from other mechanical/electrical components, and background noise. The incipient impulses which excite as well as modulate the resonance frequency of the system are easily masked by compounded effects of periodic interferences and noise, making it challenging to do a reliable fault diagnosis. As such, this paper proposes an envelope demodulation method termed short time fractal dimension (STFD) transform for fault feature extraction from such vibration signal mixture. STFD transform calculation related issues are first addressed. Then, by STFD, the original signal can be quickly transformed into a STFD representation, where the envelope of fault-induced impulses becomes more pronounced whereas interferences are partly weakened due to their morphological appearance differences. It has been found that the lower the interference frequency, the less effect the interference has on STFD representations. When interference frequency keeps increasing, more effects on STFD representations will be resulted. Such effects can be reduced by the proposed kurtosis-based peak search algorithm (KPSA). Therefore, bearing fault signature is kept and interferences are further weakened in the STFD-KPSA representation. The proposed method has been favourably compared with two widely used enveloping methods, i.e. multi-morphological analysis and energy operator, in terms of extracting impulse envelopes from vibration signals obscured by multiple interferences. Its performance has also been examined using both simulated and experimental data.

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A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Sensors ◽

10.3390/s20113105 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3105 ◽

Cited By ~ 1

Author(s):

Cong Dai Nguyen ◽

Alexander Prosvirin ◽

Jong-Myon Kim

Keyword(s):

Fault Diagnosis ◽

Reference Signal ◽

Vibration Signal ◽

Support Vector ◽

Health States ◽

Informative Signal ◽

Vibration Signals ◽

Optimal Output ◽

Gear Fault ◽

Diagnosis Method

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

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