A new intelligent fault diagnosis framework for rotating machinery based on deep transfer reinforcement learning

Yang, Daoguang; Karimi, Hamid Reza; Pawełczyk, Marek

doi:10.1016/j.conengprac.2023.105475

Cited by 17 publications

(4 citation statements)

References 34 publications

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“…The research community has focused on intelligent fault diagnosis in recent years based on the advancements in sensor technology, communication, and artificial intelligence (AI). Cumulative research has conclusively shown that the synergy between extensive datasets and advanced AI technology not only significantly enhances the precision and reliability of intelligent fault diagnosis but also effectively mitigates the inherent challenges associated with fault prediction and identification [7], [8], [9], [10], [11], [12], [13]. IFD methods are classified into three categories, Data processing, model creation, and training optimization.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments

Khan,

Akram,

Khattak

et al. 2024

Preprint

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Industry 4.0 represents the fourth industrial revolution, which is characterized by the incorporation of digital technologies, the Internet of Things (IoT), artificial intelligence, big data, and other advanced technologies into industrial processes.Industrial Machinery Health Management (IMHM) is a crucial element, based on the Industrial Internet of Things (IIoT), which focuses on monitoring the health and condition of industrial machinery. The academic community has focused on various aspects of IMHM, such as prognostic maintenance, condition monitoring, estimation of remaining useful life (RUL), intelligent fault diagnosis (IFD), and architectures based on edge computing. Each of these categories holds its own significance in the context of industrial processes. In this survey, we specifically examine the research on RUL prediction, edge-based architectures, and intelligent fault diagnosis, with a primary focus on the domain of intelligent fault diagnosis. The importance of IFD methods in ensuring the smooth execution of industrial processes has become increasingly evident. However, most methods are formulated under the assumption of complete, balanced, and abundant data, which often does not align with real-world engineering scenarios. The difficulties linked to these classifications of IMHM have received noteworthy attention from the research community, leading to a substantial number of published papers on the topic. While there are existing comprehensive reviews that address major challenges and limitations in this field, there is still a gap in thoroughly investigating research perspectives across RUL prediction, edge-based architectures, and complete intelligent fault diagnosis processes. To fill this gap,we undertake a comprehensive survey that reviews and discusses research achievements in this domain, specifically focusing on IFD. Initially, we classify the existing IFD methods into three distinct perspectives: the method of processing data, which aims to optimize inputs for the intelligent fault diagnosis model and mitigate limitations in the training sample set; the method of constructing the model, which involves designing the structure and features of the model to enhance its resilience to challenges; and the method of optimizing training, which focuses on refining the training process for intelligent fault diagnosis models and emphasizes the importance of ideal data in the training process. Subsequently, the survey covers techniques related to RUL prediction and edge-cloud architectures for resource-constrained environments. Finally, this survey consolidates the outlook on relevant issues in IMHM, explores potential solutions, and offers practical recommendations for further consideration.

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Section: Introductionmentioning

confidence: 99%

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments

Khan,

Akram,

Khattak

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, Yao et al proposed adaptive residual CNN to detect fault of small modular reactors [10] on a novel located loss that could push filters in the last convolutional layer to learn major features without any annotations for supervision [14]. Generally, the multiscale residual CNN can effectively reduce noise and fully extract features from different scales of the input data, and thus receives prominent effects when it is utilized to identify one-dimensional signals in fault diagnosis field [11][12][13][14][15]. However, it is still need to be deeply studied on how to improve accuracy when the CNN-based methods are used to detect internal leakage fault, particularly when the hydraulic cylinder operates under non-stationary load and velocity conditions and the number of monitoring signals with different forms are relatively small throughout the flight.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, convolutional neural networks (CNNs) is gradually being developed as a computational framework to detect fault of mechanical and electrical products. For example, Yao et al proposed adaptive residual CNN to detect fault of small modular reactors [10] on a novel located loss that could push filters in the last convolutional layer to learn major features without any annotations for supervision [14]. Generally, the multiscale residual CNN can effectively reduce noise and fully extract features from different scales of the input data, and thus receives prominent effects when it is utilized to identify one-dimensional signals in fault diagnosis field [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A method to detect internal leakage of hydraulic cylinder by combining data augmentation and multiscale residual CNN

He,

Ruan,

Pan

et al. 2023

The Journal of Engineering

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Developing a method to detect internal leakage in hydraulic cylinder, which is used for Electro‐Hydrostatic Actuators (EHA), is important to prevent serious malfunctions for aircrafts. At present, the internal leakage in an EHA cannot be accurately detected only using operational data. This paper proposed a convolutional neural networks (CNN) based method to detect internal leakage in hydraulic cylinder according to the relationship between operational state parameters of EHA and leakage in the hydraulic cylinder. A method was presented to align multi‐source signals with different forms by using the motor current as a benchmark. Because the number of monitoring signals are relatively small, a feedforward neural network (FFNN) based data augment method is proposed to increase parameters of input data set. A general method on how to detect internal leakage by combining signals alignment, data augmentation and multiscale residual CNN was proposed. The experimental results show that the proposed method can be used to accurately detect internal leakage in a hydraulic cylinder operating under non‐stationary load and velocity conditions, and the detection accuracy reached 99.8%.

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“…The diagnosis process is tedious and lengthy, and needs professional people to control. With the advantages of high accuracy and automatic feature extraction, the newly emerging deep learning rapidly replaces the machine learning method [8]. At present, the commonly used methods for fault diagnosis have been improved from traditional methods to using machine learning algorithms for diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A SENet-TSCNN model developed for fault diagnosis considering squeeze-excitation networks and two-stream feature fusion

Pan,

Sun,

Cheng

et al. 2023

Meas. Sci. Technol.

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The increase in the number of channels for extracting bearing fault features can to some extent enhance diagnostic performance. Therefore, this article proposes a SENet (Squeeze and Excitation Network) - TSCNN (Two Flow Convolutional Neural Network) model with high accuracy and generalization characteristics for fault diagnosis of rolling bearings. Firstly, use convolutional pooling layers to construct a basic diagnostic model framework. Secondly, due to the unsatisfactory performance of feature extraction solely on one-dimensional frequency domain signals or two-dimensional time-frequency signals, there may be misjudgments. Therefore, a dual stream convolutional model is integrated to process both one-dimensional and two-dimensional data. Fast Fourier Transform (FFT) is used to process one-dimensional frequency domain data, and Continuous Wavelet Transform (CWT) is used to process two-dimensional time-frequency maps. Once again, integrating the SENet module into the dual stream diagnostic model, the addition of attention mechanism can enable the model to better understand key features of input data. Finally, the data obtained from the processing of two channels is fused and classified in the Softmax layer. This article uses the rolling bearing fault standard data from Case Western Reserve University (CWRU) and the American Society for Mechanical Fault Prevention Technology (MFPT), and verifies through multiple controlled experiments that the model established in this article has high accuracy and good generalization characteristics.

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A new intelligent fault diagnosis framework for rotating machinery based on deep transfer reinforcement learning

Cited by 17 publications

References 34 publications

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments

A method to detect internal leakage of hydraulic cylinder by combining data augmentation and multiscale residual CNN

A SENet-TSCNN model developed for fault diagnosis considering squeeze-excitation networks and two-stream feature fusion

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A new intelligent fault diagnosis framework for rotating machinery based on deep transfer reinforcement learning

Cited by 17 publications

References 34 publications

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management &amp; Fault Diagnosis in Resource-Constrained Environments

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management &amp; Fault Diagnosis in Resource-Constrained Environments

A method to detect internal leakage of hydraulic cylinder by combining data augmentation and multiscale residual CNN

A SENet-TSCNN model developed for fault diagnosis considering squeeze-excitation networks and two-stream feature fusion

Contact Info

Product

Resources

About

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments