Comparison of motor fault diagnosis performance using RNN and K-means for data with disturbance

Choi, Dong-Jin; Han, Ji‐Hoon; Park, Sang-Uk; Hong, Sun-Ki

doi:10.23919/iccas50221.2020.9268271

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…For a relation between the motor speed and vibration signals, [5] proposes a CNN based deep learning approach for automatic motor fault diagnosis. In the same research line [6] establishes a comparison of fault motor diagnosis using RNN (Recurrent Neural Networks) and k-means in vibration analysis.…”

Section: Related Workmentioning

confidence: 99%

Automatic Anomaly Detection in Vibration Analysis Based on Machine Learning Algorithms

Torres,

Ramalho,

Correia

2022

Innovations in Mechatronics Engineering II

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This paper presents an approach for automatic anomaly detection through vibration analysis based on machine learning algorithms. The study focuses on induction motors in a predictive maintenance context, but can be applied to other domains. Vibration analysis is an important diagnostic tool in industrial data analysis to predict anomalies caused by equipment defects or in its use, allowing to increase its lifetime. It is not a new technique and is widely used in the industry, however with the Industry 4.0 paradigm and the need to digitize any process, it gains relevance to automatic fault detection. The Isolation Forest algorithm is implemented to detect anomalies in vibration datasets measured in an experimental apparatus composed of an induction motor and a coupling system with shaft alignment/misalignment capabilities. The results show that it is possible to detect anomalies automatically with a high level of precision and accuracy.

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Section: Related Workmentioning

confidence: 99%

Automatic Anomaly Detection in Vibration Analysis Based on Machine Learning Algorithms

Torres,

Ramalho,

Correia

2022

Innovations in Mechatronics Engineering II

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“…Consequently, experts began to study models for forecasting nonlinear network flow data. Among the nonlinear prediction methods, the model based on neural network (NN) theory has proved to be effective and widely used in this field 6–8 . In recent years, to overcome some inherent problems researchers found in the model based on NNs, experts have proposed some improved NN models, and it has been proved that the prediction results of these prediction methods are faster and more accurate.…”

Section: Introductionmentioning

confidence: 99%

“…Among the nonlinear prediction methods, the model based on neural network (NN) theory has proved to be effective and widely used in this field. [6][7][8] In recent years, to overcome some inherent problems researchers found in the model based on NNs, experts have proposed some improved NN models, and it has been proved that the prediction results of these prediction methods are faster and more accurate. Literature 9 is improved and optimized based on echo state network (ESN), which is a kind variant of recurrent neural network (RNN), to compensate for the characteristic defects different from the previous regular data flow and improve the prediction accuracy.…”

mentioning

confidence: 99%

Network traffic prediction model based on improved VMD and PSO‐ELM

Shi¹,

Zhou²,

Feng³

et al. 2023

Int J Communication

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SummaryThe rapid update of computing power leads to exponential data traffic growth, and the incidence of network attacks is also increasing. It is significantly important to analyze and predict network traffic accurately in the early stage and take corresponding preventive measures. The existing network flow integrated forecasting models still have some bottlenecks that are difficult to solve, for example, the slow optimization speed of modal decomposition parameters, easy falling into local optimal solutions, the slow convergence speed of the training process, and poor generalization capability. In this paper, particle swarm optimization (PSO) is utilized to improve the parameters selection process of the variational mode decomposition (VMD) algorithm and the extreme learning machine (ELM) algorithm. First, the PSO‐VMD combined with multi‐scale permutation entropy (MPE) is utilized to decompose the original network flow, and multiple eigenmode components are obtained. Second, the PSO‐ELM is utilized to train the network traffic prediction model, and the PSO parameters in PSO‐ELM are updated through adaptive weight adjustment and synchronous learning factors to increase the training and prediction speed, and the component prediction results are reconstructed to get a high‐precision network flow forecasting result. Finally, through the prediction and verification of the public network flow data of the WIDE backbone, the result of this experiment indicates that the VMD‐PSO‐ELM can break through the bottlenecks of slow optimization speed of VMD decomposition parameters, reduce the computational complexity of ELM, accelerate the convergence speed, and increase the forecasting accuracy.

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“…Driven by massive data and neural networks, DL can automatically perform high-dimensional abstract learning and the self-iterative update of the network. The recurrent neural network (RNN) is good at processing time-dependent sequence due to the recurrent structure with memory ability, realizing the information extraction through parameter sharing at different time [27]. However, RNN can only keep short-term memory due to the disappearance of gradient, thus S. Hochreiter et al added a cell state to form the long short-term memory (LSTM) network [28].…”

Section: Introductionmentioning

confidence: 99%

VMD and Self-attention Mechanism Based Bi-LSTM Model for Fault Detection of Optical Fiber Composite Submarine Cables

Feng

et al. 2022

Preprint

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As the main electrical equipment of offshore power grids, optical fiber composite submarine cables undertake the task of power transmission and data communication. In order to ensure the proper functioning of the submarine cable, it is necessary to analyze the working state of it and identify and locate the fault event. This paper proposes a fault detection method for submarine cables, that is, the VMD and self-attention based Bi-LSTM model. First, we use the ANSYS software to generate the vibration waveforms of three main fault events of optical fiber composite submarine cables. Then by generating the detection matrix of additive white gaussian noise (AWGN) and the vibration waveforms, it can realize the orientation and detection of fault events in single submarine cable. In addition, the vibration signal can be decomposed into IMF components using variational mode decomposition (VMD) for feature extraction. Moreover, the IMF components are input to the self-attention layer for feature fusion and Bi-LSTM module for further feature extraction. Finally, the result of the fault detection is output through the classification layer. According to the comparative experiment and ablation experiment, the proposed model has proved to outperform the other benchmark models and is robust and stable under the condition of different signal-to-noise ratios (SNRs).

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Comparison of motor fault diagnosis performance using RNN and K-means for data with disturbance

Cited by 7 publications

References 7 publications

Automatic Anomaly Detection in Vibration Analysis Based on Machine Learning Algorithms

Automatic Anomaly Detection in Vibration Analysis Based on Machine Learning Algorithms

Network traffic prediction model based on improved VMD and PSO‐ELM

VMD and Self-attention Mechanism Based Bi-LSTM Model for Fault Detection of Optical Fiber Composite Submarine Cables

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