Motor Fault Detection and Diagnosis Using Fuzzy Cognitive Networks with Functional Weights

Karatzinis, Georgios; Boutalis, Yiannis S.; Karnavas, Yannis L.

doi:10.1109/med.2018.8443043

Cited by 16 publications

(7 citation statements)

References 26 publications

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“…In the field of fault diagnosis, deep learning plays an extremely important role. a method using fuzzy cognitive networks (FCN) with functional weights was proposed by G. Karatzinis et al [15]. Motor currents were extracted from two phases to obtain eight features, all in the time domain.…”

Section: Deep Learning-based Fault Diagnosis Methodsmentioning

confidence: 99%

A new class of fault diagnosis method for wind turbines based on multiscale convolutional neural networks

Tong

Wang

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

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The structure of wind turbine is complicated and the operating environment is harsh, which leads to the high probability of failure of wind turbine, and the maintenance is more difficult. Once an accident occurs, it will force the wind turbine to shut down, causing huge economic losses. In addition, it will affect the safe and reliable operation of the whole power system. Therefore, fault diagnosis of wind power generation system is very important. The traditional fault diagnosis algorithm can only identify the known types of faults in wind turbines. If a new fault category appears in the wind turbine generator, the traditional fault diagnosis algorithm can only identify it as a known fault category. To solve this problem, a new classification fault diagnosis method based on semi-supervised deep learning is proposed. The benchmark model of offshore wind turbine is built in FAST software, and 10 different types of faults such as sensors and systems are simulated through model simulation. Different responses of fault signals are analyzed, and 15 parameter signals are selected as input. Firstly, fault features are extracted through multi-scale convolutional self-coding network. Secondly, the initial model is established by using the compressed feature and the error feature map as the input of the classifier and the detector respectively. Finally, the detector judges and puts the new category of fault instances into the buffer. When the buffer reaches the maximum value and starts to overflow, the algorithm starts to update, so as to realize the diagnosis task of new types of faults. Compared with the experimental results of isolated forest, local anomaly factor and single-class support vector machine, the accuracy of our proposed method and other indicators are significantly better, with an accuracy of 99.2%, which can effectively solve the problem of new class fault identification of wind turbines. It can effectively solve the problem of fault identification of the new type of fan, and is conducive to avoiding the occurrence of shutdown accidents, thus maintaining the safe and reliable operation of the power system.

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Section: Deep Learning-based Fault Diagnosis Methodsmentioning

confidence: 99%

A new class of fault diagnosis method for wind turbines based on multiscale convolutional neural networks

Tong

Wang

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

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“…In the paper proposing the dataset, to extract the features, Fast Fourier Transform (FFT) and power spectral density (PSD) are performed on vibration and motor current signal. After feature extraction and feature selection,18 features emerge for motor current signals, and 15 features are extracted for the vibration signal data [29]. Using conventional machine learning approaches, Karatzinis et al, achieved the highest accuracy of 98%.…”

Section: Introductionmentioning

confidence: 99%

FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification

Magar

Ghule

et al. 2021

IEEE Access

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The increased presence of advanced sensors on the production floors has led to the collection of datasets that can provide significant insights into machine health. An important and reliable indicator of machine health, vibration signal data can provide us a greater understanding of different faults occurring in mechanical systems. In this work, we analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods and coupling them with machine learning techniques to classify different types of bearing faults. We also highlight the importance of using different signal processing methods and their effect on accuracy for bearing fault detection. Apart from the traditional machine learning algorithms we also propose a convolutional neural network FaultNet which can effectively determine the type of bearing fault with a high degree of accuracy. The distinguishing factor of this work is the idea of channels proposed to extract more information from the signal, we have stacked the 'Mean' and 'Median' channels to raw signal to extract more useful features to classify the signals with greater accuracy.

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“…Traditional methods require feature extraction of the bearing vibration signal, dimension reduction and classification, which lead to complex mathematical models. To automate the process, machine learning (ML) methods have been introduced, such as the k-nearest neighbors (KNNs) [9], the adaptive neuro-fuzzy inference system (ANFIS) [10], fuzzy cognitive networks (FCNs) [11], the multi-agent system (MAS) approach using intelligent classifiers [12] and the support vector machine (SVM) [13]. In recent years, deep learning methods and associated techniques have been achieving dramatically increased popularity among the research areas of neural networks and artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors

Alexakos

Karnavas

Drakaki

et al. 2021

MAKE

Self Cite

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The most frequent faults in rotating electrical machines occur in their rolling element bearings. Thus, an effective health diagnosis mechanism of rolling element bearings is necessary from operational and economical points of view. Recently, convolutional neural networks (CNNs) have been proposed for bearing fault detection and identification. However, two major drawbacks of these models are (a) their lack of ability to capture global information about the input vector and to derive knowledge about the statistical properties of the latter and (b) the high demand for computational resources. In this paper, short time Fourier transform (STFT) is proposed as a pre-processing step to acquire time-frequency representation vibration images from raw data in variable healthy or faulty conditions. To diagnose and classify the vibration images, the image classification transformer (ICT), inspired from the transformers used for natural language processing, has been suitably adapted to work as an image classifier trained in a supervised manner and is also proposed as an alternative method to CNNs. Simulation results on a famous and well-established rolling element bearing fault detection benchmark show the effectiveness of the proposed method, which achieved 98.3% accuracy (on the test dataset) while requiring substantially fewer computational resources to be trained compared to the CNN approach.

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Motor Fault Detection and Diagnosis Using Fuzzy Cognitive Networks with Functional Weights

Cited by 16 publications

References 26 publications

A new class of fault diagnosis method for wind turbines based on multiscale convolutional neural networks

A new class of fault diagnosis method for wind turbines based on multiscale convolutional neural networks

FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification

A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors

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