Rotating machinery fault detection and diagnosis based on deep domain adaptation: A survey

Zhang, Siyu; Su, Lei; Gu, Jiefei; Li, Ké; Zhou, Lang; Pecht, Michael

doi:10.1016/j.cja.2021.10.006

Cited by 47 publications

(11 citation statements)

References 119 publications

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“…Additionally, investigations of fault-tolerant control systems in certain application fields are currently expanded such as in the field of underwater vehicles [18], octorotor UAVs [19], regional aircrafts [20], chemical reactors [21], wind turbines [22], fault-tolerant permanent magnet motors [23] and the power steering of forklifts [24]. Furthermore, reviews in certain fields are published that supplement the reviews mentioned above; these reviews concern the fault diagnosis of machines with small and imbalanced data [25], fault prediction and location methods in electrical energy distribution networks [26], rotating machinery fault detection and diagnosis applying deep domain adaptation [27] as well as intelligent fault-diagnosis for high-speed trains [28].…”

Section: State Of the Art In Fault-tolerant Controlmentioning

confidence: 99%

Algorithms and Methods for the Fault-Tolerant Design of an Automated Guided Vehicle

Stetter

2022

Sensors

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Researchers around the globe have contributed for many years to the research field of fault-tolerant control; the importance of this field is ever increasing as a consequence of the rising complexity of technical systems, the enlarging importance of electronics and software as well as the widening share of interconnected and cloud solutions. This field was supplemented in recent years by fault-tolerant design. Two main goals of fault-tolerant design can be distinguished. The first main goal is the improvement of the controllability and diagnosability of technical systems through intelligent design. The second goal is the enhancement of the fault-tolerance of technical systems by means of inherently fault-tolerant design characteristics. Inherently fault-tolerant design characteristics are, for instance, redundancy or over-actuation. This paper describes algorithms, methods and tools of fault-tolerant design and an application of the concept to an automated guided vehicle (AGV). This application took place on different levels ranging from conscious requirements management to redundant elements, which were consciously chosen, on the most concrete level of a technical system, i.e., the product geometry. The main scientific contribution of the paper is a methodical framework for fault-tolerant design, as well as certain algorithms and methods within this framework. The underlying motivation is to support engineers in design and control trough product development process transparency and appropriate algorithms and methods.

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Section: State Of the Art In Fault-tolerant Controlmentioning

confidence: 99%

Algorithms and Methods for the Fault-Tolerant Design of an Automated Guided Vehicle

Stetter

2022

Sensors

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“…To summarize the current research of intelligent FDP, there are a number of outstanding surveys on the topic of intelligent FDP [ 1 , 7 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. They conduct extensive review on existing literature quantitatively and qualitatively from their unique viewpoints, and identify the trends and ideas of FDP methods for different scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…The aforementioned review work provides a very good foundation for the work in this paper. Some surveys concentrate on FDP for specific type of device, e.g., machinery [ 20 , 21 , 22 , 23 , 24 , 27 , 28 ], wind power converter [ 25 ], lithium-ion battery system [ 26 ], while some focus on specific FDP method, e.g., deep domain adaptation [ 21 ], attention mechanism [ 22 ], recurrent neural network (RNN) [ 23 ], etc. Most of these reviews cover the data-driven ML techniques, but few of them give a comprehensive overview of the generic DL techniques used for industrial FDP.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Qiu

Cui

Ping

et al. 2023

Sensors

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Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the captured sensory data, and also predict their failures in advance, which can greatly help to take appropriate actions for maintenance and avoid serious consequences in industrial systems. In recent years, deep learning methods are being widely introduced into FDP due to the powerful feature representation ability, and its rapid development is bringing new opportunities to the promotion of FDP. In order to facilitate the related research, we give a summary of recent advances in deep learning techniques for industrial FDP in this paper. Related concepts and formulations of FDP are firstly given. Seven commonly used deep learning architectures, especially the emerging generative adversarial network, transformer, and graph neural network, are reviewed. Finally, we give insights into the challenges in current applications of deep learning-based methods from four different aspects of imbalanced data, compound fault types, multimodal data fusion, and edge device implementation, and provide possible solutions, respectively. This paper tries to give a comprehensive guideline for further research into the problem of intelligent industrial FDP for the community.

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“…The complex working conditions lead to frequent failures of rotating machinery, and if the failures are not detected in time, the * Author to whom any correspondence should be addressed. damaged rotating machinery components will directly affect the performance of the equipment and even cause huge economic losses [2]. Therefore, the fault diagnosis of rotating machinery is of great practical importance.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rotating machinery via multi-structure fusion discriminative projection

Zhang

Zhu

et al. 2023

Meas. Sci. Technol.

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Rotating machinery is one of the important parts of industrial production equipment, and it is of great practical significance for fault diagnosis. Aiming at the problem of classification difficulty caused by feature interleaving in complex working conditions and high-dimension of rotating machinery fault samples, a rotating machinery fault diagnosis method based on Multi-structure Fusion Discriminative Projection (MFDP) is proposed. MFDP constructed intraclass and interclass hypergraph structures with multivariate relationships, fully revealing the higher-order association information among multiple samples. Besides, a tangential graph structure of MFDP is further constructed by combining the tangential affine of local samples to preserve the local tangential information of the manifold space. In the method, a unified objective optimization model of the discriminative hypergraph structures and local tangential graph structures is developed, and by solving the model, we can obtain fault structure features with well intraclass compactness and interclass separability. Extensive experiments on the Case Western Reserve University bearing dataset and Connecticut gear dataset show that the method has a good diagnostic accuracy of rotating machinery in different working conditions.

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Rotating machinery fault detection and diagnosis based on deep domain adaptation: A survey

Cited by 47 publications

References 119 publications

Algorithms and Methods for the Fault-Tolerant Design of an Automated Guided Vehicle

Algorithms and Methods for the Fault-Tolerant Design of an Automated Guided Vehicle

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Fault diagnosis of rotating machinery via multi-structure fusion discriminative projection

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