Deep Learning-Based Machinery Fault Diagnostics

Chen, Hongtian; Ran, Guangtao; Cheng, Chao

doi:10.3390/machines10080690

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…For example, Cinar [ 522 ] proposed using transfer learning models for equipment condition monitoring. Chen et al [ 523 ] investigated the latest deep learning based methods for machinery fault diagnostics. Wang et al [ 524 ] proposed a wavelet-based CNN to achieve automatic machinery fault diagnosis.…”

Section: Deep Learning In Diverse Intelligent Sensor Based Systemsmentioning

confidence: 99%

Deep Learning in Diverse Intelligent Sensor Based Systems

Zhu

Wang

Yin

et al. 2022

Sensors

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Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.

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Section: Deep Learning In Diverse Intelligent Sensor Based Systemsmentioning

confidence: 99%

Deep Learning in Diverse Intelligent Sensor Based Systems

Zhu

Wang

Yin

et al. 2022

Sensors

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show abstract

“…Furthermore, it enhances the stability of the machine tool processing and serves as a valuable reference in the repair and maintenance of the equipment [1]. Currently, the field of CNC machine tool failure diagnosis is starting to use deep learning techniques that are good at feature learning and pattern recognition [2]. Many researchers have made significant contributions to local data prediction and fault identification in CNC machine tools [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Fault Prediction Method for CNC Machine Tools Based on SE-ResNet-Transformer

Wu,

He,

Wang

et al. 2024

Machines

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Aiming at the problem that predicted data do not reflect the operating status of computer numerical control (CNC) machine tools, this article proposes a new combined model based on SE-ResNet and Transformer for CNC machine tool failure prediction. Firstly, the Transformer model is utilised to build a non-linear temporal feature mapping using the attention mechanism in multidimensional data. Secondly, the predicted data are transformed into 2D features by the SE-ResNet model, which is adept at processing 2D data, and the spatial feature relationships between predicted data are captured, thus enhancing the state recognition capability. Through experiments, data involving the CNC machine tools in different states are collected to build a dataset, and the method is validated. The SE-ResNet-Transformer model can accurately predict the state of CNC machine tools with a recognition rate of 98.56%. Results prove the effectiveness of the proposed method in CNC machine tool failure prediction. The SE-ResNet-Transformer model is a promising approach for CNC machine tool failure prediction. The method shows great potential in improving the accuracy and efficiency of CNC machine tool failure prediction. Feasible methods are provided for precise control of the state of CNC machine tools.

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RETRACTED: An acoustic imaging recognition based cutting tools wear state prediction method

Hou,

Zhang,

Zhong

et al. 2024

IFS

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Online monitoring of cutting tools wear is an important component of advanced manufacturing technology, which can greatly improve the processing efficiency and reduce the production cost. In this paper, a cutting tools wear state prediction method based on acoustic imaging recognition is developed. By applying the advantages of the functional generalized inverse beamforming method in the sound field reconstruction, the acoustic signal is used as the carrier to reconstruct the three-dimensional space radiated sound field. And then, slice the reconstructed sound field image and input it into the convolutional neural network model as a sample, to process and classify the image and mines the feature information related to state from the sound field image. By incorporating amplitude and phase information of the sound field, the presented method utilizes spatial domain mapping to accurately identify the noise source and address challenges such as low recognition rate and difficult diagnosis under weak fault conditions. Furthermore, the paper also demonstrates the recognition of sound field states through a fault experiment in sound box simulation, based on these theories. And the recognition of sound field states is achieved through a simulation fault experiment conducted on the sound box, thereby validating the feasibility of the state monitoring method based on pattern recognition of sound and image. Finally, the experimental object is selected as the four-edge carbide milling cutter, and the cutting tools wear state is monitored by integrating sound field reconstruction techniques with convolution feature extraction methods to validate the robustness of the proposed approach.

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Deep Learning-Based Machinery Fault Diagnostics

Abstract: In recent years, deep learning has shown its unique potential and advantages in feature extraction and pattern recognition [...]

Cited by 3 publications

References 15 publications

Deep Learning in Diverse Intelligent Sensor Based Systems

Deep Learning in Diverse Intelligent Sensor Based Systems

A Fault Prediction Method for CNC Machine Tools Based on SE-ResNet-Transformer

RETRACTED: An acoustic imaging recognition based cutting tools wear state prediction method

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