A novel method of composite multiscale weighted permutation entropy and machine learning for fault complex system fault diagnosis

He, Cheng; Wu, Tao; Liu, Changchun; Chen, Tong

doi:10.1016/j.measurement.2020.107748

Cited by 24 publications

(10 citation statements)

References 20 publications

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“…The classical machine learning algorithms: BP, DT, SVM and LSSVM, the intelligent optimization algorithms improved Y NL and NS [189] Y Small-sample, NL [190] Y NL and NS, impulsive noise [191] Y NL and NS [192] -Big data, quantum Intelligent optimization algorithms-SVM [194] Improve SVM parameters Slow optimization speed, many adjustment parameters -Mixed noise [195] Y Complex imbalanced data [196] Y NL and NS [197] -Multi-channel signals [199] Y EFS [200] Y NL and NS [201] Y SVM and the deep learning algorithms: CNN and LSTM are compared from the key features, application difficulties and application occasions (table 3). By combing and comparing the common methods of fault feature identification, the classical machine learning algorithms and deep learning algorithms can achieve better application results in different occasions.…”

Section: Comparative Analysis Of Fault Feature Identification Methodsmentioning

confidence: 99%

“…Wei et al [195] proposed an imbalanced fault diagnosis framework based on cluster-majority weighted minority oversampling technique and moth-flame optimization-LSSVM, which had higher fault diagnosis identification rate and algorithm robustness. He et al [196] adopted gravitational search algorithm based on multiple adaptive constraint strategy to optimize LSSVM, which improved the accuracy of fault diagnosis. In order to realize the fault diagnosis of bearing under multi-directional loads, Zhao et al [197] proposed a fault feature extraction method based on improved multivariate VMD combined with multivariate composite multiscale weighted PE, and combined with PSO-SVM to achieve higher fault diagnosis accuracy.…”

Section: Fault Feature Identification Based On Intelligent Optimizati...mentioning

confidence: 99%

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Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

Li,

Luo,

Bai

2024

Meas. Sci. Technol.

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Rolling bearings are critical components that are prone to faults in the operation of rotating equipment. Therefore, it is of utmost importance to accurately diagnose the state of rolling bearings. This review comprehensively discusses classical algorithms for fault diagnosis of rolling bearings based on vibration signal, focusing on three key aspects: data preprocessing, fault feature extraction, and fault feature identification. The main principles, key features, application difficulties, and suitable occasions for various algorithms are thoroughly examined. Additionally, different fault diagnosis methods are reviewed and compared using the Case Western Reserve University (CWRU) bearing dataset. Based on the current research status in bearing fault diagnosis, future development directions are also anticipated. It is expected that this review will serve as a valuable reference for researchers aiming to enhance their understanding and improve the technology of rolling bearing fault diagnosis.

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Section: Comparative Analysis Of Fault Feature Identification Methodsmentioning

confidence: 99%

Section: Fault Feature Identification Based On Intelligent Optimizati...mentioning

confidence: 99%

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

Li,

Luo,

Bai

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Fault diagnosis refers to the detection of the running status of the device to find exceptions and analyze the exceptions to maintain the device to ensure that the device can work properly [10][11][12][13]. With the increasing importance of fault diagnosis in equipment maintenance, more machine learning algorithms are introduced into the field of fault diagnosis [14][15][16].…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning-Based Approach for Elevator Door System Fault Diagnosis

Liang

Chen

Wang

et al. 2022

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

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The door system is the core part of the elevator. An accurate diagnosis of the door system can aid engineers in troubleshooting and reduce maintenance costs. However, the research of fault diagnosis based on elevator operation and maintenance data is still in its infancy. With the development of the industrial Internet-of-things, real-time monitoring data of elevator can be collected and used for fault diagnosis modeling. This paper investigates a machine learning-based approach to achieve accurate elevator door fault diagnosis. An experimental study was conducted based on the monitoring data collected from the real-world elevator door system. The experimental results revealed that XGBoost algorithm can accurately identify the fault type of the elevator door.

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“…4,5 The increasing complexity of the working environment, long-term operation, and environmental factors will cause specific damage to machinery and equipment, resulting in reduced industrial production efficiency, economic losses, and even casualties. 6,7 Due to the advantages of intelligent fault diagnosis (IFD) methods in identifying the health status of machines, a lot of research has been done on them.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood graph embedding interpretable fault diagnosis network based on local and non-local information balanced under imbalanced samples

Sun

Wang

Sun

et al. 2022

Structural Health Monitoring

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Currently, the monitoring of the health status of mechanical systems is becoming more and more critical, and the actual monitoring data is massive and high-dimensional, and these data are characterized mainly by imbalance. To solve the problem of low fault recognition rate caused by data imbalance, this article proposed a novel local and non-local information balanced neighborhood graph embedding interpretable deep autoencoder (LGBNGEDAE) method for rotating machinery fault diagnosis. Specifically, the local and non-local neighborhood information graph is embedded into the original objective function of the deep autoencoder to smooth the manifold structure of the data in LGBNEDSAE, which endows the deep learning model with better data learning capability and robustness of feature extraction as well as more reasonable network interpretability, making it more suitable for feature learning and classification of unbalanced data. Further, the method’s excellent fault diagnosis capability and generalization performance are verified on two opposed experimental cases of gearboxes. Compared with other deep learning architectures and shallow learning models, the method performs better in the face of unbalanced datasets.

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A novel method of composite multiscale weighted permutation entropy and machine learning for fault complex system fault diagnosis

Cited by 24 publications

References 20 publications

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

A Machine Learning-Based Approach for Elevator Door System Fault Diagnosis

Neighborhood graph embedding interpretable fault diagnosis network based on local and non-local information balanced under imbalanced samples

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