An Improved CEEMDAN Time‐Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing

Bie, Fengfeng; Miao, Yi; Lyu, Fengxia; Peng, Jian; Guo, Yue

doi:10.1155/2021/7461402

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…The time-domain characteristic values of the vibration signal were calculated and analyzed, and six dimensioned parameter indexes were selected, including absolute mean, rms value, variance, peak-peak, skewness value and steepness value, a total of 5 dimensionless parameter indexes of peak index, margin index, pulse index, waveform index and steepness index, and four frequency domain characteristic parameter indicators including center of gravity frequency, mean square frequency and frequency standard deviation were selected to form the time frequency domain fault characteristics [11][12][13][14] .…”

Section: Fault Feature Extractionmentioning

confidence: 99%

Fault diagnosis of reciprocating compressor based on multi-scale permutation entropy

Jiang¹,

Zhang²,

Qiao³

2023

International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022)

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Reciprocating compressor is a kind of power machinery widely used in the industrial field, in order to achieve the condition monitoring of reciprocating compressor and fault diagnosis of key components, the reciprocating compressor vibration signal acquisition experimental platform is designed, and the mechanical performance of the compressor is monitored by means of vibration detection and signal analysis. In addition, a fault feature extraction method integrating time domain, frequency domain and entropy value is proposed, the fault feature extraction of the processed vibration signal is carried out, the extracted fault feature is used as input, and the compressor fault diagnosis is carried out by using the limit learning machine algorithm, and the results show that the method can better diagnose and identify the faults of different parts of the reciprocating compressor.

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Section: Fault Feature Extractionmentioning

confidence: 99%

Fault diagnosis of reciprocating compressor based on multi-scale permutation entropy

Jiang¹,

Zhang²,

Qiao³

2023

International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022)

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“…As classical signal processing methods, EEMD and CEEMDAN are often used by researchers to compare with the proposed methods. 48–50 To verify the effectiveness of ICEEMDAN in fault diagnosis, the original vibration signal and the feature vectors extracted by EEMD, CEEMDAN, and ICEEMDAN are used as the input of Ghost-IRCNN model, and the accuracies are 95.33%, 97.75%, 98.27%, and 99.74% respectively. The feature vectors extracted by ICEEMDAN has the highest diagnostic accuracy in Ghost-IRCNN model, which is 4.44%, 1.99%, and 1.47% higher than the other three methods.…”

Section: Experimental Analysismentioning

confidence: 99%

Intelligent diagnosis method of bearing fault based on ICEEMDAN and Ghost-IRCNN

Zou

Jin

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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As a critical component of rotating machinery, the health status of bearings is of great significance to ensure the safety of machine operation. Since the vibration signals are interfered by noise during the machine operation, the traditional bearing fault diagnosis model has insufficient feature extraction ability, low diagnostic accuracy, and slow convergence speed. An intelligent bearing fault diagnosis method is proposed based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and inverted residual convolution neural network integrated with Ghost module (Ghost-IRCNN). Firstly, ICEEMDAN is used to decompose the signals, and the kurtosis-correlation coefficient-margin factor index is proposed to filter and reconstruct the important modal components. Then, the multi-scale sample entropy is used to construct the feature vectors. Finally, the Ghost module, convolution block attention module, and inverted residual bottleneck are combined to build Ghost-IRCNN to realize bearing fault classification and diagnosis. The experimental results show that the proposed method can extract bearing fault features effectively, and the diagnosis accuracy can reach more than 98.80% under complex conditions.

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“…Accurate monitoring and judgement on rolling bearing health, especially on the early failure stage, is vital to maintain the reliability of machinery and ensure the safe operation of equipment 2,3 . Fault diagnosis algorithms of bearings based on signal processing and machine learning have been the research hotpot over recent years 4 .…”

Section: Introductionmentioning

confidence: 99%

Research of fault information fusion based on ICEEMDAN and PSO- SVM algorithm for rolling bearing diagnosis

Bie

Zhang

Lyu

et al. 2023

Preprint

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As a key component of mechanical system, the extraction and accurate identification of fault characteristic information of rolling bearing is very important to ensure its normal operation. The diagnosis accuracy is occasionally low due to the limitation of information collected by a single type of data source. In this paper, the bearing vibration signal and acoustic emission signal are employed as analysis sources, a novel method based on ICCEMDAN (improved complete ensemble empirical mode decomposition with adaptive noise) with optimized SVM (support vector machine) is presented for the fault information fusion, feature extraction, and fault pattern recognition of rolling bearing. Firstly, ICEEMDAN algorithm is developed to decompose the rolling bearing vibration signal and acoustic emission signal for a series of IMF (intrinsic mode function) components. Secondly, the valuable components that can characterize the original signal status are selected based on the correlation coefficient-variance contribution criterion. Thirdly, the singular spectral entropy of the reconstructed component is calculated as the eigenvalue and the two signal eigenvectors are fused as a new eigenvector set. Finally, the feature vector set is input into the optimized SVM classifier model based on PSO optimization for training and pattern recognition, in which the accuracy and efficiency of the classifier model and SVM classifier model are compared. Study of model simulation and fault simulation experiments show that the presented model based on the singular value entropy fusion of ICEEMDAN and PSO-SVM can effectively extract the fault characteristics of rolling bearing signals and has a desired performance in the accurate pattern recognition.

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An Improved CEEMDAN Time‐Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing

Cited by 9 publications

References 15 publications

Fault diagnosis of reciprocating compressor based on multi-scale permutation entropy

Fault diagnosis of reciprocating compressor based on multi-scale permutation entropy

Intelligent diagnosis method of bearing fault based on ICEEMDAN and Ghost-IRCNN

Research of fault information fusion based on ICEEMDAN and PSO- SVM algorithm for rolling bearing diagnosis

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