Feature extraction of rotor fault based on EEMD and curve code

Liu, Dong; Zhang, Xiao; Hu, Xiao; Cong-xin, Zhang; Malik, O.P.

doi:10.1016/j.measurement.2018.12.009

Cited by 39 publications

(16 citation statements)

References 35 publications

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“…EEMD is similar to EMD and is a self-adaptive method of signal decomposition [26], [27]. However, due to the end effect in the decomposition process, false IMFs will be generated, which will badly affect the extraction of fault feature and the accuracy of fault diagnosis.…”

Section: B Obtaintrue Imfs Using Cloud Model Methodsmentioning

confidence: 99%

A Novel Rolling Bearing Defect Detection Method Based on Bispectrum Analysis and Cloud Model-Improved EEMD

et al. 2020

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Mechanical signals are not only disturbed by Gaussian noise, but also by non-Gaussian noise. These Gaussian noise and non-Gaussian noise have gravely impeded detecting of rolling bearing defects using traditional methods. In this context, the paper develops a novel detection method for rolling bearing, which combines bispectrum analysis with an improved ensemble empirical mode decomposition (EEMD). To effectively eliminate Gaussian noise in the signal, bispectrum analysis is adopted. In order to effectively reduce non-Gaussian noise, a cloud model-improved EEMD is proposed, where the cloud model is introduced to restrain the mode mixing phenomenon. Then a rolling bearing defect detection plan based on the proposed method is put forward. From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in reducing multiple background noise. Furthermore, compared with other three methods, the results show that the proposed method can detect the defect of rolling bearings more effectively. INDEX TERMS Bispectrum analysis, cloud model, defect detection, ensemble empirical mode decomposition, mode mixing, rolling bearing.

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Section: B Obtaintrue Imfs Using Cloud Model Methodsmentioning

confidence: 99%

A Novel Rolling Bearing Defect Detection Method Based on Bispectrum Analysis and Cloud Model-Improved EEMD

et al. 2020

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“…Rotor systems are essential mechanical components in various industrial areas, such as power generation and manufacturing. In order to prevent unexpected failures and to improve the reliability of rotor systems, significant recent research efforts have been made to develop data-driven fault diagnosis techniques for rotor systems, many based on the growth of data-related technologies [1], [2]. In conventional data-driven diagnosis methods, the signal processing and feature engineering steps are conducted to extract essential information that can accurately represent the health states of rotor systems.…”

Section: Introductionmentioning

confidence: 99%

Direct Connection-Based Convolutional Neural Network (DC-CNN) for Fault Diagnosis of Rotor Systems

Kim

Jung

et al. 2020

IEEE Access

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Fault diagnosis of rotor systems is important to prevent unexpected failures. Recently, deep learning (DL) methods, such as a convolutional neural network (CNN), have been utilized in many research areas, including fault diagnosis. DL has gained significant attention thanks to its ability to efficiently learn proper features from input data. It is possible to learn enriched hierarchical features by making the DL architectures deeper; therefore, many studies have been conducted to stack the neural networks, which are the basic building blocks of DL, deeper. However, it becomes difficult to comprehensively train neural network architectures as they become deeper, due to problems in the flow of gradient information during the training phase. In this paper, a direct connection based CNN (DC-CNN) method is proposed to significantly improve training efficiency and diagnosis performance. DC-CNN connects feature maps of different layers within a CNN to improve the gradient information flow over the layers. These additional connections, however, can increase the number of trainable parameters within the network. To prevent problems that might be caused by an increased number of parameters, dimension reduction modules are also developed. Moreover, to consider the anisotropic characteristics inherent in rotor systems, the vibration images containing both spatial and temporal information are generated and utilized. The effectiveness of DC-CNN is validated using experimental data from a rotor testbed. The experimental results indicate that the proposed method outperforms other conventional approaches with a smaller number of parameters. Also, visualizations of the learned features indicate that the proposed method can learn much more effective and significant features. Furthermore, the proposed method outperforms other approaches under conditions of insufficient or noisy data.

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“…The ensemble empirical mode decomposition (EEMD) algorithm maintains the adaptive decomposition of the empirical mode decomposition (EMD) and overcomes the endpoint effect and mode aliasing effect of EMD [40,41]. However, the selection of the intrinsic mode function (IMF) is still a problem to be solved [42,43].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation on Valve Leakage of Reciprocating Compressor Using System Characteristic Diagnosis Method

et al. 2020

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High impact and strong noise complicate the response of reciprocating compressor (RC). It requires a complex signal processing method that is a single response-based or excitation-based fault diagnosis method applied to RC valve leakage fault diagnosis. This paper proposes a quantitative diagnosis method of RC valve leakage that is based on system characteristic diagnosis method. First, the current signal of the RC induction motor and the cylinder vibration signal are introduced as the excitation and response signals, the mathematical model of the RC motor current is established, and the influence mechanism of the valve leakage on the RC vibration is analyzed. Subsequently, the ensemble empirical mode decomposition and comb filter are respectively used to extract the fault characteristic information of excitation signal and response signal to obtain the excitation condition indicators (CIs), response CIs, and system CIs. Finally, the support vector machine based on the obtained CIs classified the valve leakage failure patterns of different severity, and a fault diagnoser was constructed for the quantitative diagnosis of valve leakage fault. The results of experiment and application proved that the proposed method could realize the quantitative diagnosis of RC valve leakage fault while using simple signal processing technology.

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Feature extraction of rotor fault based on EEMD and curve code

Cited by 39 publications

References 35 publications

A Novel Rolling Bearing Defect Detection Method Based on Bispectrum Analysis and Cloud Model-Improved EEMD

A Novel Rolling Bearing Defect Detection Method Based on Bispectrum Analysis and Cloud Model-Improved EEMD

Direct Connection-Based Convolutional Neural Network (DC-CNN) for Fault Diagnosis of Rotor Systems

Quantitative Evaluation on Valve Leakage of Reciprocating Compressor Using System Characteristic Diagnosis Method

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