fault diagnosis of servo drive system of CNC machine based on deep learning

Zhang, Zerui; Cao, Shuxin; Cao, Jun

doi:10.1109/cac.2018.8623472

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Zhang et al [18] proposed an autoencoder model to tackle the problem of permanent magnet synchronous machine (PMSM) fault detection in CNC machines, achieving an accuracy of 100%. However, their study did not focus on realtime fault detection.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing Fault Detection in CNC Machinery: A Deep Learning and Genetic Algorithm Approach

Mbilong,

Aarab,

Belouadha

et al. 2023

ISI

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This paper introduces a deep learning-based approach (DLBA) tailored for fault detection and condition monitoring in industrial machinery. The presented DLBA architecture is assessed utilizing a dataset derived from a CNC milling machine, as part of the University of Michigan's System-level Manufacturing and Automation Research Testbed (SMART). The results underscore the substantial efficacy of the LSTM model, evidenced by a precision of 94% and an F1 score of 95%. These findings serve as a robust foundation for the identification of CNC machining failures within the manufacturing industry. The DLBA architecture constitutes a comprehensive framework for efficient fault detection, incorporating a variety of network models, including MLP, CNN, CNN auto-encoder, LSTM, and ResNet. Each model leverages its unique strengths in the analysis of complex data. Genetic Algorithm (GA) optimization is employed for parameter tuning across all models, enhancing their performance. The findings from this study contribute significantly to the development of more reliable and cost-effective predictive maintenance systems, enabling manufacturers to detect faults early on, thus preventing expensive downtime and mitigating safety risks.

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Section: Related Workmentioning

confidence: 99%

“…The present study casts its focus on fault diagnosis in CNC machines [15][16][17][18][19], an area that is attracting burgeoning interest in the sphere of industrial research. Despite significant strides in the realm of fault detection, conspicuous gaps remain evident.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Fault Detection in CNC Machinery: A Deep Learning and Genetic Algorithm Approach

Mbilong,

Aarab,

Belouadha

et al. 2023

ISI

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“…Feature extraction and selection are other issues in machine learning classification. Many feature extraction methods have been proposed using principal components analysis (PCA) [ 13 , 17 , 18 ], autoencoders (AEs) [ 18 , 19 , 20 , 21 , 22 ], and convolutional neural networks (CNNs) [ 23 ]. PCA requires less calculation and has faster responses and AE is suitable for situations lacking negative sampling.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Deep Learning by MCU Implementation: An Intelligent Gripper for Shape Identification

Hung

Zeng

Lee

et al. 2021

Sensors

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This paper introduces a real-time processing and classification of raw sensor data using a convolutional neural network (CNN). The established system is a microcontroller-unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two embedded accelerometers to sense acceleration (in the form of vibration signals) on the jaws for identification. The raw data is firstly transferred into images by short-time Fourier transform (STFT), and then the CNN algorithm is adopted to extract features for classifying objects. In addition, the hyperparameters of the CNN are optimized to ensure hardware implementation. Finally, the proposed artificial intelligent model is implemented on a MCU (Renesas RX65N) from raw data to classification. Experimental results and discussions are introduced to show the performance and effectiveness of our proposed approach.

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“…However, the compression characteristics of AE also have good performance in feature extraction. The feature extraction of AE is used for the position information of the permanent magnet synchronous motor, then the ANN and SVM are used to diagnose whether the motor is overloaded or lacks lubrication [11]. The feature extraction of AE is used to diagnose the system state of the polisher and classify the reason for the error [12].…”

Section: Introductionmentioning

confidence: 99%

Design of a Chamfering Tool Diagnosis System Using Autoencoder Learning Method

Hung

Mao

et al. 2019

Energies

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. In this paper, the autoencoder learning method is proposed for the system diagnosis of chamfering tool equipment. The autoencoder uses unsupervised learning architecture. The training dataset that requires only a positive sample is quite suitable for industrial production lines. The abnormal tool can be diagnosed by comparing the output and input of the autoencoder neural network. The adjustable threshold can effectively improve accuracy. This method can effectively adapt to the current environment when the data contain multiple signals. In the experimental setup, the main diagnostic signal is the current of the motor. The current reflects the torque change when the tool is abnormal. Four-step conversions are developed to process the current signal, including (1) current-to-voltage conversion, (2) analog-digital conversion, (3) downsampling rate, and (4) discrete Fourier transform. The dataset is used to find the best autoencoder parameters by grid search. In training results, the testing accuracy, true positive rate, and precision approach are 87.5%, 83.33%, and 90.91%, respectively. The best model of the autoencoder is evaluated by online testing. The online test means loading the diagnosis model in the production line and evaluating the model. It is shown that the proposed tool can effectively detect abnormal conditions. The online assessment accuracy, true positive rate, and precision are 75%, 90%, and 69.23% in the original threshold, respectively. The accuracy can be up to 90% after adjusting the threshold, and the true positive rate and precision are up to 80% and 100%, respectively.

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fault diagnosis of servo drive system of CNC machine based on deep learning

Cited by 4 publications

References 5 publications

Enhancing Fault Detection in CNC Machinery: A Deep Learning and Genetic Algorithm Approach

Enhancing Fault Detection in CNC Machinery: A Deep Learning and Genetic Algorithm Approach

End-to-End Deep Learning by MCU Implementation: An Intelligent Gripper for Shape Identification

Design of a Chamfering Tool Diagnosis System Using Autoencoder Learning Method

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