Open-circuit fault detection for three-phase inverter based on backpropagation neural network

Ji, Zhendong; Liu, Wei

doi:10.1007/s00521-018-3663-2

Cited by 20 publications

(8 citation statements)

References 22 publications

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“…Besides, the double chain quantum genetic algorithm can be utilized to optimise the current length and the denoising sparse autoencoder can extract the fault feature automatically [127], [128]. In [129], each phase current is shifted by 120 degrees and 240 degrees and performed the Clark transformation to generate the direct currents in d-q axis. Wavelet decomposition is another widely adopted method to extract the fault feature in both timeand frequency-domain.…”

Section: A: Feature Extractionmentioning

confidence: 99%

“…The BP neural network is the most common one in the literature which consists of one hidden layer [129], [130]. Nevertheless, the performance of the BP neural network is not so satisfying.…”

Section: B: Networkmentioning

confidence: 99%

“…Nevertheless, the performance of the BP neural network is not so satisfying. For example, further steps have to be performed on the outputs of the BP neural network to confirm the fault diagnosis results in some cases [129]. Ensemble learning is an optional solution to improve the performance of neural networks.…”

Section: B: Networkmentioning

confidence: 99%

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Ensuring a Reliable Operation of Two-Level IGBT-Based Power Converters: A Review of Monitoring and Fault-Tolerant Approaches

Liu

Yang

et al. 2020

IEEE Access

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Despite the emerging multi-phase and multi-level converters, two-level insulated gate bipolar transistor-based power converters are still widely used in industrial applications in nowadays. Thus, its reliability is of significant importance for ensuring industrial safety. In this paper, a review of the possibilities to ensure the reliable operation of these power converters is presented. The possible approaches are categorized into two groups: condition monitoring and fault-tolerant control. The former approach performs the monitoring methods of power converters, which enables the identification of device degradation to be realized. Accordingly, the mechanisms, indicators, and measuring methods of degradation are demonstrated in this paper to assist the design of condition-based maintenance. In contrast, the latter approach is a postfault one, where power converters remain the operation by activating the fault-tolerant units after faults are identified. For this approach, fault detection, fault isolation, and tolerant strategies are essential. Finally, the performance and cost-effectiveness of the two categories are discussed in this paper. INDEX TERMS Two-level power converter, reliability, condition Monitoring, fault tolerance, renewable energy application, insulated gate bipolar transistor.

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Section: A: Feature Extractionmentioning

confidence: 99%

Section: B: Networkmentioning

confidence: 99%

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Ensuring a Reliable Operation of Two-Level IGBT-Based Power Converters: A Review of Monitoring and Fault-Tolerant Approaches

Liu

Yang

et al. 2020

IEEE Access

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“…The fan-based diagnosis based on the vibration signals are convenient to detect, have wide applicability and have gained the researchers' interest by being widely used for fan fault monitoring and diagnosis. The conventional methods of fault detection and diagnosis are primarily focused on time domain or frequency domain analysis; however, utilization of fan's non-stationary characteristics have provided the third dimension in fault analysis domain [2]. Using the fan-based fault analysis, the low magnitude signal can easily be compared to the background noise and thus, overcoming the limitation of the general signal analysis methods by the utilization of vibration signal-based fan diagnosis [3,4].…”

Section: Introductionmentioning

confidence: 99%

Research on fan vibration fault diagnosis based on image recognition

Huang¹,

Qiao²,

Khanna³

et al. 2021

J. vibroeng.

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The conventional methods for vibration fault detection and diagnosis relies on feature extraction from the waveforms of the vibration signals. This article exploits the scope of image recognition application for the detection and diagnosis of fan vibration faults. In this paper, a novel image recognition technique is proposed for vibration-based fault diagnosis using the spectrum images of the vibration signals. 1D vibration signal spectrum is initially computed using Fast Fourier Transform (FFT) and the FFT frequencies are adjusted such that it captures a vibration spectrum diagram as 2D image representation. FFT based vibration analysis is done and the image recognition concept is utilized for feature extraction and a machine learning classification module is used for fault analysis and diagnosis. Effective feature generation is done using Principal Component Analysis (PCA) by removing the redundancy from the feature vectors and machine learning classifiers are used to obtain improved image recognition and classification performance. Artificial Neural Network (ANN) classifier yields better performance in terms of various performance parameters and percentage improvement in terms of accuracy for ANN classification methods over Support Vector Machine (SVM), k-Nearest Neighbours (kNN) and Random Forest Ensemble (RFE) methods are 10.01 %, 4.51 % and 2.01 % respectively. Comparative scenarios are considered in this work for fan vibration fault detection as well as diagnosis based on the image features for various realistic vibration fault conditions. Effectiveness of the proposed image recognition-based technique is compared with the state-of-the-art methods, justifying its outperformance for fan fault detection and diagnosis using the combination of spectrum adjustment, PCA and ANN classification method.

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“…It can improve the structure of convolutional neural networks, use two kinds of loss functions for network training and feature extraction, and finally improve the recognition performance of partial fingerprint images. To realize real-time fault detection in power devices and enhance reliability of inverter circuits, Ji and Liu [21] proposed a detection method based on Park's transform algorithm and neural network. Lin et al [22] study automatically crawls the information published by users of the MedHelp Medical Forum and then combines it with disease-related user posts obtained from Twitter.…”

mentioning

confidence: 99%

Special issue on emergence in human-like intelligence toward cyber-physical systems

Yen

2019

Neural Comput & Applic

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As the famous slogan ''Connecting People'' indicates, a lot of the developments in novel technologies intensify the relationship between people without necessarily enhancing technologies that are close to the nature of human beings. Examples can be easily found in recent computing paradigms, such as Cloud Computing that advances network infrastructure for data storage and resource sharing, or the Internet of Things that investigates the intelligence and awareness of objects involved in the network. From this point of view, the existing state-of-the-art solutions, in realms of artificial intelligence and/or computational intelligence, significantly differ from the human competence level in many research domains. Even though it is generally not clear whether human-like approach would show its upper-hand over existing methods, the exploration of this research path seems to be advantageous and challenging. Human-like intelligence is arguably one of the most powerful approaches for the optimization and design of complex Web, as well as its correlated issues so as to meet the requirements and growing needs of new challenging applications (and services as well). Although the state of the art behind the computational intelligence has already reached an impressive scale, there remain quite a lot of attractive research topics, with consideration of human-like or human-oriented factors, for further and promising developments.The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. This special issue will be published by Neural Computing and Applications as special issues.

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Open-circuit fault detection for three-phase inverter based on backpropagation neural network

Cited by 20 publications

References 22 publications

Ensuring a Reliable Operation of Two-Level IGBT-Based Power Converters: A Review of Monitoring and Fault-Tolerant Approaches

Ensuring a Reliable Operation of Two-Level IGBT-Based Power Converters: A Review of Monitoring and Fault-Tolerant Approaches

Research on fan vibration fault diagnosis based on image recognition

Special issue on emergence in human-like intelligence toward cyber-physical systems

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