Detection of Bearing Faults Using a Novel Adaptive Morphological Update Lifting Wavelet

Li, Yifan; Zuo, Ming J.; Feng, Ke; Chen, Yuejian

doi:10.1007/s10033-017-0186-1

Cited by 18 publications

(5 citation statements)

References 27 publications

(27 reference statements)

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“…Baidu, in particular, still has 81% accuracy for speech recognition in very noisy environments. This result is far superior to other famous companies such as Google [ 15 ]. Natural language processing is a technology that enables people to communicate with computers directly through language.…”

Section: Rolling Bearing Fault Detection Systemmentioning

confidence: 84%

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Rolling Bearing Fault Detection System and Experiment Based on Deep Learning

Zhang

2022

Computational Intelligence and Neuroscience

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The current situation of frequent small-scale accidents shows that the existing methods have not completely solved the problem of bearing failures, and new research methods need to be used to complete the study of bearing failures. To prevent the failure of rolling bearings and meet the need for timely detection of faults, this research is based on deep learning. Using the combination of deep transfer learning and metric learning methods, the identification and analysis of bearing multi-state vibration signals under different working conditions are carried out. The combination of SSAE-based similarity measurement criteria and deep transfer learning can reduce the differences between different domains. It is difficult to distinguish the data samples at the boundary and diagnose the problems that the physical meaning is difficult to understand. Through the bearing fault diagnosis analysis, the validity of the deep learning diagnosis model proposed in this paper is verified. The results show that the detection accuracy of the rolling bearing fault detection method based on LCM-SSAE is 0.6 percentage points higher than that of the rolling bearing fault detection method based on SSAE, which proves that the method is suitable for the fault detection of rolling bearing, and it also shows the effectiveness and robustness of the fault detection system of rolling bearing.

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Section: Rolling Bearing Fault Detection Systemmentioning

confidence: 84%

“…Baidu, in particular, still has 81% accuracy for speech recognition in very noisy environments. This result is far superior to other famous companies such as Google [ 15 ].…”

Section: Rolling Bearing Fault Detection Systemmentioning

confidence: 84%

Rolling Bearing Fault Detection System and Experiment Based on Deep Learning

Zhang

2022

Computational Intelligence and Neuroscience

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“…Morphological filtering is widely used in image processing [18], speech recognition, fault diagnosis, and other fields [19], [20]. In recent years, it has been applied in detecting transient signals and achieved good application effects [21], [22], especially in expanding morphological operators' construction, the optimal SE selection and adaptive morphological filter [23], which has become a research hotspot [24]- [27]. The morphological filter combined with the structural element (SE) as the filtering window [28] to matche the geometric features of the signal to be analyzed.…”

Section: Introductionmentioning

confidence: 99%

Transient Electromagnetic Weak Signal Extraction Method Based on Multi-Scale Combined Difference Product Morphological Filtering

et al. 2022

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To solve the problem of poor recognition effect of transient signal in low Signal-to-Noise ratio(SNR) and strong interference electromagnetic environment, a morphological filtering method based on the multi-scale combined difference product (MCDPMF) was proposed. This paper concentrates on the issues of sudden changes in transient electronic signal, such as impulses and edges. Firstly, it provides a difference product morphological filter. Moreover, the extended and multi-origin morphological Structural Elements (SEs) is constructed, combining with the multi-structural layers a (a indicates the structural layers of the MCDPMF), the transient electromagnetic weak signal is multi-scale filtered. They are used to optimize the number of the structure layers a adaptively based on the amplitude characteristic ratio of the positive and negative polarity of the filtered signal(HML value), combining the kurtosis-SNR(k x − SN R) ratio characteristic coefficient. MCDPMF is proposed to enhance the filtering results and suppress the noise frequency points. Meanwhile, it can extract the structure components and identify the features of the transient electromagnetic weak signal. It can be shown from simulation and experimental results that the proposed method is superior to EMD, AVG, OCCO, and other methods in subjective evaluation and objective indicators.

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“…e mathematical morphological lter has been widely used in the elds of digital image processing [8] and mechanical systems [9][10][11]. At the same time, it has also been fully used in medical signal processing [12].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction for Evaluating the Complexity of Electromagnetic Environment Based on Adaptive Multiscale Morphological Gradient and Nonnegative Matrix Factorization

Mai

et al. 2022

Journal of Electrical and Computer Engineering

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In the current battlefield space, with the massive application of electromagnetic equipment, the electromagnetic environment in the battlefield space tends to be complex, which can lead to the electromagnetic equipment and personnel in the battlefield space receiving interference from the electromagnetic environment signal. To protect the safety of personnel and equipment quality, it is necessary to evaluate the complexity of the electromagnetic environment signal research, to use the corresponding measures. However, there is still little research related to the evaluation of the complexity of electromagnetic environmental signals. In this paper, a feature extraction method for electromagnetic environmental signals based on adaptive multiscale morphological gradient filtering and a nonnegative matrix factorization algorithm is proposed. First, the electromagnetic environment signal is filtered by AMMG, and then the filtered signal is processed by NMF for feature extraction. Finally, the complex electromagnetic environment signals after feature extraction are evaluated and classified by the SVM method. The results show that the evaluation results have good classification accuracy, and this paper provides an effective feature extraction method for the complexity of electromagnetic environment signals.

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Detection of Bearing Faults Using a Novel Adaptive Morphological Update Lifting Wavelet

Cited by 18 publications

References 27 publications

Rolling Bearing Fault Detection System and Experiment Based on Deep Learning

Rolling Bearing Fault Detection System and Experiment Based on Deep Learning

Transient Electromagnetic Weak Signal Extraction Method Based on Multi-Scale Combined Difference Product Morphological Filtering

Feature Extraction for Evaluating the Complexity of Electromagnetic Environment Based on Adaptive Multiscale Morphological Gradient and Nonnegative Matrix Factorization

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