Simultaneous fault diagnosis of wind turbine using multichannel convolutional neural networks

Zare, Samira; Ayati, Moosa

doi:10.1016/j.isatra.2020.08.021

Cited by 55 publications

(12 citation statements)

References 23 publications

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“…The integration of the techniques of artificial intelligence such as neural networks is investigated using the examples of spacecrafts [13], distributed power generators [14] and transmission lines [15], industrial robots [16] and bearings [17]. 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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“…Multi-source data are captured by an adaptive convolution kernel matching the number of data channels, meanwhile, an irregular convolution kernel is introduced to expand the field of view of FAC-CNN. Zare et al [32] considered changes in each measurement variable and identified subsystem failures, they used a multi-channel convolutional neural network with multiple parallel local heads. The image composed of the time domain signal obtained from the wind turbine is input into the convolutional neural network.…”

Section: Related Workmentioning

confidence: 99%

Adaptive feature extraction and fault diagnosis for three-phase inverter based on hybrid-CNN models under variable operating conditions

Sun

et al. 2021

Complex Intell. Syst.

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The increasing reliability and availability requirements of power electronic systems have drawn great concern in many industrial applications. Aiming at the difficulty in fault characteristics extraction and fault modes classification of the three-phase full-bridge inverter (TFI) that used as the drive module of brushless DC motor (BLDCM). A hybrid convolutional neural network (HCNN) model consists of one-dimensional CNN (1D-CNN) and two-dimensional CNN (2D-CNN) is proposed in this paper, which can tap more effective spatial feature for TFI fault diagnosis. The frequency spectrum from the three-phase current signal preprocess are applied as the input for 1D-CNN and 2D-CNN to conduct feature extraction, respectively. Then, the feature layers information are combined in the fully connected layer of HCNN. Finally, the performance status of TFI could be identified by the softmax classifier with Adam optimizer. Several groups of experiments have been studied when the BLDCM under different operating conditions. The results show that the fusion features can get a higher degree of discrimination so as to the presented network model also obtains better classification accuracy, which verify the feasibility and superiority to the other networks.

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“…Convolutional neural networks (CNN) has been applied on manufacturing use case and proved their adaptability to the environment's conditions [9]- [11]. In a binary classification, where the dataset is composed of two classes, a CNN will learn the specific features of a class.…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

Welding Seam Classification in the Automotive Industry using Deep Learning Algorithms

Hachem

Perrot

Painvin

et al. 2021

2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

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Welding seam inspection is key process in the automotive industry and should guarantee the quality required by the client. Visual inspection is achieved by the operator who checks each part manually, making the reliability highly improvable. That's why automating the visual inspection is needed in today's production process. Collecting data from inside the plant may not provide a balanced number of images between good welding seams and bad welding seams. In this article, we will compare a standard deep learning algorithm applied on raw data with data augmentation approaches. Our target is to reach an accuracy of 97% on the defected reference parts. This target is reached on some welds, while it remains a challenge on other welds.

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Simultaneous fault diagnosis of wind turbine using multichannel convolutional neural networks

Cited by 55 publications

References 23 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

Adaptive feature extraction and fault diagnosis for three-phase inverter based on hybrid-CNN models under variable operating conditions

Welding Seam Classification in the Automotive Industry using Deep Learning Algorithms

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