Recurrent Neural Network Based On-line Fault Diagnosis Approach for Power Electronic Devices

Xu, Xiang; Chen, Ruqing

doi:10.1109/icnc.2007.599

Cited by 5 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, empirical mode decomposition (EMD) is an effective method to deal with nonlinear and non-stationary signals. Its main advantage is to decompose complex signals into several intrinsic mode functions (IMF) arranged from high to low frequency, which overcomes the difficulty of selecting basis functions in wavelet transform, but it can not overcome the problem of modal aliasing caused by noise [29].In order to solve the mode aliasing problem, N.E Huang added the Gauss white noise assisted decomposition method on the basis of EMD, which is the ensemble empirical mode decomposition(EEMD). Its basic principle is to use the uniform distribution characteristics of Gauss white noise spectrum, add the white noise into the whole time-frequency distribution space, and take the "average" of the results of multiple decomposition.…”

Section: Eemd Ensemble Empirical Mode Decomposition Methodsmentioning

confidence: 99%

Bearing Fault Diagnosis Method Based on EEMD and LSTM

Ping¹,

Hou²,

Jiang³

et al. 2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

The condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system.In the past few years, deep neural network (DNN) has been recognized as an effective tool to detect rolling bearing faults. However, It is too complex to directly feed the original vibration signal to the DNN neural network, and the accuracy of fault identification is not high. By using the signal preprocessing technology, the original signal can be effectively removed and preprocessed without losing the key diagnosis information. In this paper, a new EEMD-LSTM bearing fault diagnosis method is proposed, which combines the signal preprocessing technology with the EEMD method that can get clear fault feature signals, and LSTM technology to extract fault features automatically that improves the efficiency of fault feature extraction. In the case of small sample size, this method can significantly improve the accuracy of fault diagnosis.

show abstract

Section: Eemd Ensemble Empirical Mode Decomposition Methodsmentioning

confidence: 99%

Bearing Fault Diagnosis Method Based on EEMD and LSTM

Ping¹,

Hou²,

Jiang³

et al. 2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

show abstract

“…The RNN is a neural network model that excels in processing time series and boasts fast convergence, high accuracy, and high stability. In terms of defect diagnosis, the RNN is particularly well suited to complicated equipment or systems [68][69][70][71].…”

Section: Application Of Recurrent Neural Network (Rnn)mentioning

confidence: 99%

“…Using diagonal RNNs, the literature [68] presents a method for diagnosing interturn defects in the stator windings of asynchronous motors. RNNs with deviation units are used in the literature [69] to implement distortion voltage waveforms based on rectifiers. This method for diagnosing complex power electronic equipment or systems has been shown to be useful through fault classification and in experiments.…”

Section: Application Of Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review

et al. 2021

View full text Add to dashboard Cite

Electric motors are used extensively in numerous industries, and their failure can result not only in machine damage but also a slew of other issues, such as financial loss, injuries, etc. As a result, there is a significant scope to use robust fault diagnosis technology. In recent years, interesting research results on fault diagnosis for electric motors have been documented. Deep learning in the fault detection of electric equipment has shown comparatively better results than traditional approaches because of its more powerful and sophisticated feature extraction capabilities. This paper covers four traditional types of deep learning models: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN), and highlights their use in detecting faults of electric motors. Finally, the issues and obstacles that deep learning encounters in the fault detection mechanism as well as the prospects are discussed and summarized.

show abstract

“…Other architectures include the application of structures similar to MLP, but with the feedback loops inserted. Such networks are used to solve similar problems as more traditional approaches, like the analysis of induction motor [28] or power electronics elements [29]. − Hierarchical Neural Network (HMM) -this is the more complex structure [30] exploiting ANN architectures presented above.…”

Section: Diagnostic Applications Of Annmentioning

confidence: 99%

Diagnostics of analog systems using Artificial Neural Networks

Bilski¹

2017

PAR

View full text Add to dashboard Cite

ŎƬ˘˹ŘǚŘ ʊǔǄ ȭŘ ǘɁʁ˘˿ʊǜŘȭǔƬ ˘ Řʁǜ˿ǘˁȦˁ ȭŘ ˹ŘʁˁȭǘŘƋǒ ǚǔƋƬȭƋǖǔ ,ʁƬŘǜǔ˸Ƭ ,ɁȧȧɁȭʊ Ę˘ȭŘȭǔƬ ŘˁǜɁʁʊǜ˹Ř ʯƖ1 . IntroductionDiagnostics of analog systems is currently a wide domain with numerous applications, closely related to the types of analyzed objects. With the increasing number of such systems (including mechanical and electronic elements), the set of usable approaches also increases. Contrary to the digital object, its analog counterpart is characterized by the continuous transmission and processing of information. Therefore the system may also be in the infinite number of states, generating continuous signals. This makes the diagnostics of analog systems especially difficult. The most popular approaches used in the domain belong to Artificial Intelligence (AI). Their advantages include the autonomous operation (without the input of the human operator), high accuracy with generalization abilities and (in most cases) the ability to update knowledge through the additional learning procedure. Disadvantages include the dependency of the acquired diagnostic knowledge on the available data (use cases) presented during the training of the diagnostic module. Sometimes explanation of the reasoning process (i.e. how the premises were used to draw conclusions) is required. The most popular diagnostic methods, exploited in practice, are Artificial Neural Networks (ANN) [1,2]. This paper presents the taxonomy, structural and operational details of ANN used for the diagnostic purposes. The structure of the paper is as follows. Section 2 presents diagnostic principles important from the AI-application point of view. In Section 3 the generic diagnostic architecture is introduced with the focus on internal modules. Section 4 covers the ANN work regime from the diagnostic point of view. In Section 5 the main architectures of ANN with their applications are presented. Section 6 presents diagnostics of the 5 th order lowpass filter using the RBF ANN-based classifier. In Section 7 summary and future prospects of the discussed ANN are provided. Aims of the diagnosticsDiagnostics is aimed at determining the actual state of the analyzed System Under Test -SUT, based on the observations of the measured signals y(t) being responses to the known excitation x(t). Similarly to the medical and veterinary diagnostics, in technical sciences non-invasive approaches are preferred, where the information about the system's behavior is available only through the input and output (accessible and partially accessible) nodes (with the maximum set of nodes being hidden, i.e. inaccessible). Therefore the diagnostic system is expected to establish relations (in the form of the unknown function f()) between the observable features of output signals and configuration of parameters p defining the SUT's work regime [1]. The latter can be based on the human expert's experience or the mathematical description of the system. Alternatively, simulations of the SUT model may provide the information about how it works and how changes in parameters influence behavior o...

show abstract

Recurrent Neural Network Based On-line Fault Diagnosis Approach for Power Electronic Devices

Cited by 5 publications

References 5 publications

Bearing Fault Diagnosis Method Based on EEMD and LSTM

Bearing Fault Diagnosis Method Based on EEMD and LSTM

Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review

Diagnostics of analog systems using Artificial Neural Networks

Contact Info

Product

Resources

About