Neural Network Fault Diagnosis of Voltage Source Inverter under variable load conditions at different frequencies

Dhumale, R. B.; Lokhande, Shashikant

doi:10.1016/j.measurement.2016.04.051

Cited by 65 publications

(25 citation statements)

References 22 publications

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“…In [21], a principle of low-frequency sampling of the main fault components and neural networks for classification was established for the fault diagnosis of inverters. In [22], a neural network fault diagnosis method based on current Park's Vector Transform (PVT) and dis-crete wavelet transform (DWT) was recommended. In [23], an online fault diagnosis model was offered with wavelet decomposition for processing fault current signals and SVM for classification.…”

Section: Introductionmentioning

confidence: 99%

“…In [23], an online fault diagnosis model was offered with wavelet decomposition for processing fault current signals and SVM for classification. The diagnosis methods in [18][19][20][21][22][23] focused on the open-circuit fault of a two-level inverter. However, the three-level inverter consists of more power switches than a two-level inverter, resulting in a more complex circuit structure and lower reliability.…”

Section: Introductionmentioning

confidence: 99%

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NPC Three-Level Inverter Open-Circuit Fault Diagnosis Based on Adaptive Electrical Period Partition and Random Forest

Liu

Wan

et al. 2020

Journal of Sensors

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Fault detection can increase the reliability and efficiency of power electronic converters employed in power systems. Among the converters in the power system, a Neutral Point Clamped (NPC) three-level inverter is most commonly used to drive electric motors. In this paper, a new approach for open-circuit fault detection and location of the NPC three-level inverter for a shifting process using a constant voltage-to-frequency ratio is proposed. In order to diagnose open-circuit fault in as short a time as possible, an adaptive electrical period partition (AEPP) algorithm is proposed to pick single electrical periods from real-time three-phase current signals. The Maximal Overlap Discrete Wavelet Transformation (MODWT) and Park’s Vector Modulus (PVM) are used for feature analysis and normalization of electrical period signals. The statistical characteristics of the electrical period signals are extracted, and a random forest model is constructed to realize the state classification. Compared with the traditional fault diagnosis method, the proposed algorithm finds fault locations quickly and accurately. The effectiveness and accuracy of the proposed algorithm are verified by experiments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NPC Three-Level Inverter Open-Circuit Fault Diagnosis Based on Adaptive Electrical Period Partition and Random Forest

Liu

Wan

et al. 2020

Journal of Sensors

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“…Commonly, condition monitoring involves comparing vibration signals obtained through faulty and healthy conditions. Mainly in condition monitoring fault diagnosis is primarily considered which involves data collection, data processing, fault identification and fault classification [3][4][5][6][7][8][9]. Alokkumar, et al [10] attempted to diagnose shaft misalignment using motor current signature analysis (MCSA) by conducting number of tests using spectra quest machine fault simulator.…”

Section: Introductionmentioning

confidence: 99%

Vibration analysis using wavelet transform and fuzzy logic for shaft misalignment

Umbrajkaar¹,

Krishnamoorthy²

2018

J. vibroeng.

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In most of rotating kind of machinery, misalignment is commonly observed defect. Though machines are installed to zero-zero aligned condition in initial stage, in due course of run time operation misalignment get introduced due thermal distortion, asymmetry of forces exerted and inadequate assembly of parts. This paper presents a method of misalignment prediction from output vibration signal which is based on combined DWT (Discrete Wavelet Transform) and Fuzzy Logic approach. DWT helps in multilevel decomposition of vibration signal to understand where and at what instant misalignment occurs. This involves selection of mother wavelet. The FIS (Fuzzy Inference System) is combined with DWT to predict degree of misalignment present. An inclusion of FIS has improved prediction accuracy of Degree of Misalignment (DoM) which has claimed error less than one percent (1 %).

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“…As a kind of artificial intelligence method, neural network has been widely used in the field of fault diagnosis [10][11][12]. With the ability of self-organization and self-learning, the network itself can achieve good classification.…”

Section: Introductionmentioning

confidence: 99%

Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

Yang

Wang

Feng

et al. 2017

Mathematical Problems in Engineering

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Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

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Neural Network Fault Diagnosis of Voltage Source Inverter under variable load conditions at different frequencies

Cited by 65 publications

References 22 publications

NPC Three-Level Inverter Open-Circuit Fault Diagnosis Based on Adaptive Electrical Period Partition and Random Forest

NPC Three-Level Inverter Open-Circuit Fault Diagnosis Based on Adaptive Electrical Period Partition and Random Forest

Vibration analysis using wavelet transform and fuzzy logic for shaft misalignment

Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

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