Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter

Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin

doi:10.1016/j.ymssp.2017.09.007

Cited by 71 publications

(38 citation statements)

References 45 publications

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“…e shape and size of structural element play an important role in the application of mathematical morphology. e triangular SE, the sinusoidal SE, and the linear SE are widely used SEs for signal analysis [32][33][34][35][36].…”

Section: E Shape Selection Of Structural Elementmentioning

confidence: 99%

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Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering

2020

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Nonlinear time series denoising is the prerequisite for extracting effective information from observation sequence. An effective chaotic signal denoising method not only has a good signal-to-noise ratio (SNR) enhancement performance, but also can remain as a good unpredictable denoised signal. However, the inherent characteristics of chaos, such as extreme sensitivity to initial values and broadband spectrum, pose challenges for noise reduction of polluted chaotic signals. To address these issues, an adaptive smoothing multiscale morphological filtering (ASMMF) is proposed to reconstruct chaotic signals. In the process of noise reduction for contaminated chaotic signals, firstly, a multiscale morphological filter is constructed adaptively according to the multiscale permutation entropy (MPE) of morphological filter residuals, and the contaminated signals are filtered. Secondly, the weight coefficients of scale structural element are calculated by the residual sum of squares operation, and the chaotic signals are reconstructed. Finally, the resampled filter signals are smoothed by cubic B-spline interpolation operation. In the experiment, the Lorenz signal with white Gaussian noise, the measured sunspot, and the chaotic vibration signal are reconstructed by four comparison methods. The test results show that the proposed ASMMF method has obvious advantages in noise suppression and topological trajectory restoration.

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Section: E Shape Selection Of Structural Elementmentioning

confidence: 99%

“…However, if the raw signal is processed by a scale far away from the theoretical central scale, the filtered result is often heavily polluted. Fortunately, multiscale morphological filtering with adaptive weighting can solve this problem [36]. erefore, the flat SE is therefore chosen in filtering chaotic signal.…”

Section: E Shape Selection Of Structural Elementmentioning

confidence: 99%

Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering

2020

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“…On this basis, 10 parameters of power data in each phase, viz. out-to-in value, maximum difference, mean value, root mean square, variance, sum of difference, kurtosis, crest factor, form factor, and impulse factor, are extracted respectively as the time domain statistical features of the PM, and these parameters are used in [20,21]. However, it is not sufficient to extract the feature data of the PM only from the time 8 domain.…”

Section: Statistical Feature Extractionmentioning

confidence: 99%

Degradation state mining and identification for railway point machines

Bian

Yang

Huang

et al. 2019

Reliability Engineering & System Safety

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Point machines (PMs) are used for switching and locking railway turnouts, and are considered one of the most critical elements of a railway signal system. The failure of the point mechanism directly affects the operation of the railway and may cause serious safety accidents. Hence, there is a need for early detection of the anomalies in PMs. From normal operation to complete failure, the machine usually undergoes a series of degradation states. If the degradation states are detected in time, maintenance can be organized in advance to prevent the malfunction. This paper presents a degradation detection method that can effectively mine and identify the degradation state of the PM. First, power data is processed to obtain the feature set that can describe the PM characteristics effectively. Then, a clustering analysis of the feature set is carried out by self-organizing feature-mapping network, and various degradation states are mined. Finally, the optimized support vector machine is used to build the state classifier to identify the degradation state of the PM. The experimental results obtained with the Siemens S700K PM show that the proposed method could not only mine the effective degradation states, but also obtain high identification accuracy. 2The performance of a PM directly affects the safety of train operation. In the field investigation of the Chenzhou West Station, the related faults caused by the PM were found to account for 30% of all signal equipment failures, and the average time for repairing the PM is approximately 23.4 minutes, which has a serious impact on the operation of the high-speed railway. Therefore, it is necessary to detect the faults in the PM in time and make reasonable maintenance plans to improve the operational efficiency of the high-speed railway. A condition monitoring system (CMS) can measure, centralize, and analyze the data from sensors installed in the field equipment to detect equipment failure [1]. Many railway companies have implemented CMS for PMs. However, experience in this field has shown that these systems create an alarm only when a failure occurs or very close to the point of failure. In addition, most of those currently in use do not effectively detect faults, and the system simply creates an alert to indicate abnormal behavior, leading to a delay in repairing of the PM [2]. The fault detection technology of CMS needs to be improved in order to increase the maintenance efficiency of PMs. In recent years, research on fault detection for PMs has gradually transitioned from the threshold-based method to statistical analysis, model-based, and classification methods [3]. Compared to thresholding technology, these three methods can ensure early detection of machine failure. Among them, the classification method is widely used in CMS [4].The classification method can be divided into three main steps, viz. data feature processing, classifier establishment, and fault identification. The data feature processing step converts the measurement signal of the PM, e.g. power signal,...

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“…Sugumaran et al [16] illustrated a method based on a decision tree to select bearing fault features. Li et al [17] used a multiscale morphological filter signal processing method to select a train axle bearing fault feature. As the mentioned methods have some limitations in different working conditions, they need more feature methods to adapt to various harsh working conditions.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

Zuo

Liao

2020

Mathematical Problems in Engineering

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Symptom parameter is a popular method for bearing fault diagnosis, and it plays a crucial role in the process of building a diagnosis model. Many symptom parameters have been performed to extract signal fault features in time and frequency domains, and the improper selection of parameter will significantly influence the diagnosis result. For dealing with the problem, this paper proposes a novel dominant symptom parameters selection scheme for bearing fault diagnosis based on canonical discriminant analysis and false nearest neighbor using GA filtered signal. The original signal was filtered by a genetic algorithm (GA) at first and then mapped to the new characteristic subspace through the canonical discriminant analysis (CDA) algorithm. The map distance in the new characteristic subspace is calculated by the false nearest neighbor (FNN) method to interpret the dominance of symptom parameters. The dominant symptom parameters brought to the bearing diagnosis system can improve the diagnosis result. The effectiveness of the proposed method has been demonstrated by the diagnosis model and by comparison with other methods.

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Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter

Cited by 71 publications

References 45 publications

Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering

Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering

Degradation state mining and identification for railway point machines

Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

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