LSTM Based Bearing Fault Diagnosis of Electrical Machines using Motor Current Signal

Sabir, Russell; Rosato, Daniele; Hartmann, Sven; Guehmann, Clemens

doi:10.1109/icmla.2019.00113

Cited by 44 publications

(22 citation statements)

References 19 publications

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“…LSTM can also be used for EM fault diagnosis. In [28], R. Sabir et al proposed a LSTM based method for motor bearing fault detection, which applied LSTM for feature extraction and faults classification. However, when the EM working condition changes, this fault detection model must be retrained.…”

Section: A Related Workmentioning

confidence: 99%

Novel Data-Driven Approach Based on Capsule Network for Intelligent Multi-Fault Detection in Electric Motors

Chen

Cao

et al. 2021

IEEE Trans. Energy Convers.

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With the steady development of technology, electric motors (EMs) have become one of the most important components in modern industry. To ensure stable industrial production, detecting and classifying the EM faults is crucial. A novel intelligent deep-learning-based multi-fault detection method for EMs under varying working conditions is proposed in this paper. This method involves two steps: first, a 2D convolution network without pooling layer is proposed to extract features from raw EM data. In addition, a long short-term memory (LSTM) network is applied to extract the fault features for comparison. Second, a capsule network (Caps-Net) based on a dynamic routing algorithm is used as a classifier to realize intelligent multi-fault detection and improve the generalization performance of the proposed model. The proposed method is applicable to raw physical signals of EMs, which improves the overall efficiency of the fault detection. Moreover, the proposed method has a strong generalization ability. The simulation results demonstrate that the proposed approach can achieve higher accuracy than various benchmark methods. Moreover, its fault detection accuracy is higher than those of other state-of-theart models under two working conditions, in which the load type and size of the EM are changed, respectively.

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Section: A Related Workmentioning

confidence: 99%

Novel Data-Driven Approach Based on Capsule Network for Intelligent Multi-Fault Detection in Electric Motors

Chen

Cao

et al. 2021

IEEE Trans. Energy Convers.

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“…Finally, L f requency is combined with L CNN to construct the final loss function of the GAN's generator. As shown in Equation (7), the sum of L CNN and L f requency is taken as a modification term in the general GAN's loss function L G to ensure the generated data from GAN has a high similarity and captures the important information in detail at the same time. α is a weight factor.…”

Section: Improvement Of Loss Function With Envelope Spectrummentioning

confidence: 99%

“…CNN was first employed in the bearing fault diagnosis by O. Janssens in 2016 [2], and, since then, many improvements have proposed to enhance the CNN's performance, such as 1D-CNN, 2D-CNN, multiscale CNN, and adaptive CNN [3][4][5][6]. Russell Sabir adopted LSTM for the bearing fault diagnosis based on the motor current signal and obtained a classification accuracy of 96% [7]. L. Yu and D. Qiu proposed the stacked LSTM and the bidirectional LSTM, respectively, and both LSTMs obtained an accuracy of more than 99% on the bearing fault diagnosis [8,9].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Optimization of CNN and GAN for Bearing Fault Diagnosis under Unbalanced Datasets

et al. 2021

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Convolutional Neural Network (CNN) has been widely used in bearing fault diagnosis in recent years, and many satisfying results have been reported. However, when the training dataset provided is unbalanced, such as the samples in some fault labels are very limited, the CNN’s performance reduces inevitably. To solve the dataset imbalance problem, a Generative Adversarial Network (GAN) has been preferably adopted for the data generation. In published research studies, GAN only focuses on the overall similarity of generated data to the original measurement. The similarity in the fault characteristics is ignored, which carries more information for the fault diagnosis. To bridge this gap, this paper proposes two modifications for the general GAN. Firstly, a CNN, together with a GAN, and two networks are optimized collaboratively. The GAN provides a more balanced dataset for the CNN, and the CNN outputs the fault diagnosis result as a correction term in the GAN generator’s loss function to improve the GAN’s performance. Secondly, the similarity of the envelope spectrum between the generated data and the original measurement is considered. The envelope spectrum error from the 1st to 5th order of the Fault Characteristic Frequencies (FCF) is taken as another correction in the GAN generator’s loss function. Experimental results show that the bearing fault samples generated by the optimized GAN contain more fault information than the samples produced by the general GAN. Furthermore, after the data augmentation for the unbalanced training sets, the CNN’s accuracy in the fault classification has been significantly improved.

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“…For rotating machines such as bearing [13], asynchronous motor [14], and wind turbine [15], LSTM has been utilized to conduct the condition diagnosis. Sabir et al [16] used a motor current signal to perform fault diagnosis of bearings. The bearing fault dataset from Paderborn University was used, and the four LSTM layers showed high diagnostic performance using raw data input.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Condition Diagnosis of Linear Motion Guide Using Generative Model Based on Images

2021

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Recently, linear motion (LM) guides have been widely used in industrial processes, especially for precise positioning applications. A LM guide typically requires a custom design for specific characteristics of several industrial fields, which is time consuming for manufacturing process; additionally, and the production line with failed LM guides cannot be fixed rapidly. Therefore, to reduce production loss during such periods, it is important to prepare for maintenance and management in case of a fault through a real-time diagnosis of LM guide conditions. Currently, studies on condition diagnosis applying deep learning algorithms are actively being conducted, and actual measured signals in the industrial fields are being used as training data. In this study, the condition diagnosis of LM guides is conducted through a variational autoencoder (VAE) with convolutional layers. Normal and fault data are measured using an LM guide unit that consists of four LM blocks and one ball screw. The measured signals are converted to spectrogram images through short-time Fourier transform, and only normal data are used to perform network learning. The trained model is applied to classify the normal and fault states, and the reconstruction error is utilized as the evaluation metrics for classification performance. In order to validate the performance, the results are compared to those of a restricted Boltzmann Machine (RBM), and a stacked auto-encoder and a VAE, which does not consist of a convolutional network. Through this study, it is shown that the LM guide diagnosis is possible with a network (VAE with convolution layers) learned with only normal states and the performance is shown to be superior to other networks.INDEX TERMS LM guide, fault diagnosis, variational auto-encoder, short-time Fourier transform, unsupervised learning.

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LSTM Based Bearing Fault Diagnosis of Electrical Machines using Motor Current Signal

Cited by 44 publications

References 19 publications

Novel Data-Driven Approach Based on Capsule Network for Intelligent Multi-Fault Detection in Electric Motors

Novel Data-Driven Approach Based on Capsule Network for Intelligent Multi-Fault Detection in Electric Motors

Collaborative Optimization of CNN and GAN for Bearing Fault Diagnosis under Unbalanced Datasets

Unsupervised Condition Diagnosis of Linear Motion Guide Using Generative Model Based on Images

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