Advances in Machine Fault Diagnosis

Vaimann, Toomas

doi:10.3390/app11167348

Cited by 3 publications

(2 citation statements)

References 11 publications

(11 reference statements)

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“…The tremendous success of deep learning technology in feature learning and image recognition has presented new opportunities for advancing power battery fault diagnosis in recent years. These breakthroughs can potentially drive further progress in this field [8].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Zhou,

Zhang,

Wang

2024

Processes

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The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model’s ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Zhou,

Zhang,

Wang

2024

Processes

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“…IM-related faults caused by high-frequency inverter transients and behavior that generate the faults. Academia, and industry are investing heavily in their research and development (R&D) related to condition monitoring and diagnostics [81,82]. Figure 6 shows a tie of an electric machine with an intelligent diagnostics system, which integrates the motor's real-time parameters, for example, electrical, mechanical, flux, optical, acoustic, chemical, partial discharges, and others, to form a diagnostic [83][84][85].…”

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confidence: 99%

Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques

et al. 2023

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Due to their efficiency and control capabilities, induction motors fed with inverters have become prevalent in various industrial applications. However, ensuring the reliable operation of the motor and diagnosing faults on time are crucial for preventing unexpected failures and minimizing downtime. This paper systematically analyzes condition monitoring and practical diagnostic techniques for inverter-fed motor drive systems. This study encompasses a thorough evaluation of different methods used for condition monitoring and diagnostics of induction motors, with the most crucial faults in their stator, rotor, bearings, eccentricity, shaft currents, and partial discharges. It also includes an assessment of their applicability. The presented analysis includes a focus on the challenges associated with inverter-fed systems, such as high-frequency harmonics, common-mode voltages causing the bearing currents, and high voltage gradients (dv/dt) due to fast switching frequency, which can impact the motor operation, as well as its faults analysis. Furthermore, this research explores the usefulness and efficiency of various available diagnostic methods, such as motor current signature analysis and other useful analyses using advanced signal processing techniques. This study aims to present findings that provide valuable insights for developing comprehensive condition monitoring strategies, and practical diagnostic techniques that enable proactive maintenance, enhanced system performance, and improved operational reliability of inverter-fed motor drive systems.

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Analysis of Common Mode and Rapidly Varying Voltage Profile on Stator Current Harmonics of an Inverter-fed Induction Motor

Sardar,

Vaimann,

Kütt

et al. 2023

2023 IEEE 64th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON)

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Advances in Machine Fault Diagnosis

Abstract: The growing need for intelligent machines, the outreach for more efficient use of the machines in industry, and the development of Industry 4 [...]

Cited by 3 publications

References 11 publications

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques

Analysis of Common Mode and Rapidly Varying Voltage Profile on Stator Current Harmonics of an Inverter-fed Induction Motor

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