Research on a fault diagnosis method of rolling bearings using variation mode decomposition and deep belief network

Zhao, Huimin; Liu, Hailong; Xu, Junjie; Guo, Chen

doi:10.1007/s12206-019-0811-2

Cited by 29 publications

(17 citation statements)

References 28 publications

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“…Zhao el al. [80] presented a variation mode decomposition-(VMD-) and Hilbert transform-(HT-) based DBN (VHDBN) for rolling bearing fault classification. Bearing vibration signals are decomposed into intrinsic mode functions (IMFs) through VMD.…”

Section: Deep Belief Network (Dbn)mentioning

confidence: 99%

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Soother

Daudpoto

Harris

et al. 2021

Mathematical Problems in Engineering

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The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.

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Section: Deep Belief Network (Dbn)mentioning

confidence: 99%

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Soother

Daudpoto

Harris

et al. 2021

Mathematical Problems in Engineering

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“…Gear vibration signal is nonlinear, non -stationary and has large data volume. Research on gear fault diagnosis method based on ELMD energy entropy and PSO-SAE [7] shows that it can solve such problems. Gearbox has a complicated structure and it is difficult to obtain a signal when it fails.…”

Section: Gearbox Fault Diagnosismentioning

confidence: 99%

Research Progress of Rotating Machinery Fault Diagnosis Based on Deep Learning

Lin²

2021

E3S Web Conf.

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In modern production, the precision and the importance of rotating machinery is higher and higher in the direction of large-scale, high speed and automation development, so that the traditional fault diagnosis methods are insufficient to deal with massive, multi-source and high-dimensional data, cannot meet the requirements of security and reliability. Therefore, several typical deep learning models are briefly introduced at first and the application of deep learning in fault diagnosis of rotor system, gear box and rolling bearing in recent years is studied and analyzed based on its strong feature extraction ability and advantages of clustering analysis. Finally, the advantages and disadvantages of deep learning model are summarized and the fault diagnosis methods of rotating machinery are summarized and prospected based on engineering practice.

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“…Wang et al, 107 Zhao et al, 108 Jiang et al, 109 Zhao et al, 110 and Tang et al 111 used traditional DBN to implement fault diagnosis, but the traditional DBN only considered simple one-dimensional structural information. Experts and scholars have optimized DBN for these shortcomings.…”

Section: Deep Learningmentioning

confidence: 99%

Review of intelligent fault diagnosis for permanent magnet synchronous motors in electric vehicles

Xue

Zhang

et al. 2020

Advances in Mechanical Engineering

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Permanent magnet synchronous motors are the main power output components of electric vehicles. Once a failure occurs, it will affect the vehicle’s power, stability, and safety. While as a complex field-circuit coupling system composed of machine-electric-magnetic-thermal, the permanent magnet synchronous motor of electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency, and communication characteristics make it difficult to diagnose faults. Based on the research of a list of related references, this article reviews the methods of intelligent fault diagnosis for electric vehicle permanent magnet synchronous motors. The research status and development trend of fault diagnosis are analyzed. It provides theoretical basis for motor fault diagnosis and health management in multi-variable working conditions and multi-physics environment.

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Research on a fault diagnosis method of rolling bearings using variation mode decomposition and deep belief network

Cited by 29 publications

References 28 publications

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Research Progress of Rotating Machinery Fault Diagnosis Based on Deep Learning

Review of intelligent fault diagnosis for permanent magnet synchronous motors in electric vehicles

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