An Effective Induction Motor Fault Diagnosis Approach Using Graph-Based Semi-Supervised Learning

Zaman, Shafi Md Kawsar; Liang, Xiaodong

doi:10.1109/access.2021.3049193

Cited by 40 publications

(14 citation statements)

References 20 publications

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“…The research focused on the challenges that are now being addressed and those that will be addressed in the future in the development of autonomous diagnostic methods. The LabVIEW platform was used to produce cutting-edge capabilities for online control of induction motors [ 20 , 21 , 22 ]. It has been determined that the use of stator current analysis-based demodulation methods is the most suited method for diagnosing bearing faults.…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

et al. 2022

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In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system make the motor highly reliable, helping to avoid unscheduled downtimes, which cause more revenue loss and disruption of production. This can be achieved only when the irregularities produced because of the faults are sensed at the moment they occur and diagnosed quickly so that suitable actions to protect the equipment can be taken. This requires intelligent control with a high-performance scheme. Hence, a Field Programmable Gate Array (FPGA) based on neuro-genetic implementation with a Back Propagation Neural network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classification of the neural network will provide the output within 2 µs although the clone procedure with microcontroller requires 7 ms. This intelligent control with a high-performance technique is applied to the IMD fed by a Voltage Source Inverter (VSI) to diagnose the fault. The proposed approach was simulated and experimentally validated.

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Section: Introductionmentioning

confidence: 99%

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

et al. 2022

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“…Affected by factors such as overload, aging, and complex working conditions, rolling bearings are prone to failure. Therefore, the research on the intelligent fault diagnosis of rolling bearings is of great significance to ensure the safe and stable operation of electromechanical equipment and has received great attention from experts in the field of fault diagnosis [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Cross Working Condition Multiscale Recursive Feature Fusion Method for Fault Diagnosis of Rolling Bearing in Multiple Working Conditions

Zhang

Zhou

2022

IEEE Access

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As a key component of electromechanical equipment in the intelligent manufacturing process, rolling bearings play an important role to secure a safe, stable, and efficient operation. Deep learning can be used to guide a data-driven fault diagnosis which requires that all data are independently identically distribution (i.i.d). When the equipment is operated with multiple working conditions, the collected samples violates the assumption of i.i.d, which will inevitably make it difficult to extract accurate feature involved in the data. This paper proposes a deep learninag based fault diagnosis model to recursively fuse the multiscale feature on cross working conditions, such that data without working condition label can also be referred to train a satisfying deep learning model for fault diagnosis of bearing operated in multiple working conditions. In the case when only a small number size of training samples for a separated working condition are available, the proposes fusion mechanism aims to establish a jointly learning mechanism between different working conditions. To verify the effectiveness of the proposed algorithm, experimental validation was performed using the Case Western Reserve University (CWRU) rolling bearing public data set. The experimental results show that the proposed method can make full use of a small amount of labeled data with working conditions and a large amount of labeled data without working conditions. In ten types of fault diagnosis tasks with different fault sizes, the fault diagnosis accuracy reaches more than 94% for 4 working conditions and more than 86% for 8 working conditions.INDEX TERMS Fault diagnosis, feature fusion, multiple working conditions, rolling bearing.

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“…In order to improve the fault diagnosis performance of principal component analysis (PCA), a hybrid model based on PCA and BN was developed by Tanjin et al, and it dealt with the uncertainty generated by the multivariate contribution plots, which provided better diagnostic performance over PCA. Zaman et al proposed a graph-based semi-supervised learning method for induction motor fault diagnosis . Fezai et al and Hichri et al reported the tandem forest-based method and the genetic-algorithm-based NN method, respectively, for the fault detection of industrial systems. , In short, these fault diagnosis methods based on machine learning take the correct rate of fault diagnosis as the learning target and have a wide range of applications, but the machine learning algorithm requires not only normal samples but also a large number of fault samples, and the fault diagnosis accuracy is closely related to the completeness and representativeness of the samples.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Intelligent Fault Diagnosis Model Based on Improved MPCA-V for Sensors in a Laboratory-Scale Wastewater Treatment Process

Liu

Niu

Zhou

et al. 2022

Ind. Eng. Chem. Res.

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The study objective is to propose a hybrid fault diagnosis method for a laboratory-scale sequential batch reactor (SBR) wastewater treatment process based on time-varying covariance and variable-wise unfolded MPCA method (MPCA-V), which can detect the fault batch, determine the fault time simultaneously, and further identify the fault source. To establish and validate the MPCA-V model, 50 normal batches and 55 batches including 7 fault batches were employed separately. Furthermore, the classical MPCA (MPCA-B) model was introduced for comparison. For the three detected fault batches, with the MPCA-V model, not only the fault occurring time and fault source were located and identified by the contribution degree calculation of each variable to the T 2 and SPE statistics simultaneously but also the fault detection rate was averaged as 90%, which was much higher than that of MPCA-B (67%). Introducing time dependency and correlation in a laboratory-scale SBR process gives the work practical significance and breakthrough.

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An Effective Induction Motor Fault Diagnosis Approach Using Graph-Based Semi-Supervised Learning

Cited by 40 publications

References 20 publications

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

A Cross Working Condition Multiscale Recursive Feature Fusion Method for Fault Diagnosis of Rolling Bearing in Multiple Working Conditions

Hybrid Intelligent Fault Diagnosis Model Based on Improved MPCA-V for Sensors in a Laboratory-Scale Wastewater Treatment Process

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