Deep Learning with Long Short-Term Memory Networks for Diagnosing Faults in Smart Grids

Hassani, Hossein; Razavi–Far, Roozbeh; Saif, Mehrdad; Zio, Enrico

doi:10.1007/s41125-021-00074-4

Cited by 5 publications

(2 citation statements)

References 47 publications

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“…Multiple studies have used CWT, DWT, and wavelet–based synchrosqueezing transform (WSST) as the input data to extract features of the signals [ 14 , 16 , 18 ]. Hilbert–Huang transform (HHT) and variational mode decomposition (VMD), which decompose signals into intrinsic mode functions (IMF), are effective signal analysis methods [ 17 , 23 , 24 ]. Wavelet transform (WT) and VMD are efficient methods to analyze signals changing locally, and HHT is a good method to describe instantaneous frequency signals.…”

Section: Related Workmentioning

confidence: 99%

Multi–Output Classification Based on Convolutional Neural Network Model for Untrained Compound Fault Diagnosis of Rotor Systems with Non–Contact Sensors

Son

Hong

Kim

2023

Sensors

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Fault diagnosis is important in rotor systems because severe damage can occur during the operation of systems under harsh conditions. The advancements in machine learning and deep learning have led to enhanced performance of classification. Two important elements of fault diagnosis using machine learning are data preprocessing and model structure. Multi–class classification is used to classify faults into different single types, whereas multi–label classification classifies faults into compound types. It is valuable to focus on the capability of detecting compound faults because multiple faults can exist simultaneously. Diagnosis of untrained compound faults is also a merit. In this study, input data were first preprocessed with short–time Fourier transform. Then, a model was built for classification of the state of the system based on multi–output classification. Finally, the proposed model was evaluated based on its performance and robustness for classification of compound faults. This study proposes an effective model based on multi–output classification, which can be trained using only single fault data for the classification of compound faults and confirms the robustness of the model to changes in unbalance.

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Section: Related Workmentioning

confidence: 99%

Multi–Output Classification Based on Convolutional Neural Network Model for Untrained Compound Fault Diagnosis of Rotor Systems with Non–Contact Sensors

Son

Hong

Kim

2023

Sensors

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“…This fault diagnosis model achieved very high diagnostic accuracy when applied to high-speed trains with different speeds and different faults. In [9], an efficient fault diagnosis method for smart grid systems was designed based on long short-term memory (LSTM) recurrent neural networks. And, using echo-state networks, the multiclass classification task can be processed by application of different dimensionality reduction techniques.…”

Section: Introductionmentioning

confidence: 99%

A Modified Generative Adversarial Network for Fault Diagnosis in High-Speed Train Components with Imbalanced and Heterogeneous Monitoring Data

Wang

Liu

Zio

2022

JDMD

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Data-driven methods are widely considered for fault diagnosis in complex systems. However, in practice the between-class imbalance due to limited faulty samples may deteriorate their classification performance. To address this issue, synthetic minority methods for enhancing data have been proved to be effective in many applications. Generative Adversarial Networks (GANs), capable of automatic features extraction, can also be adopted for augmenting the faulty samples. However, the monitoring data of a complex system may include not only continuous signals, but also discrete/categorical signals. Since the current GAN methods still have some challenges in handling such heterogeneous monitoring data, a Mixed Dual Discriminator GAN (noted as M-D2GAN) is proposed in this work. In order to render the expanded fault samples more aligned with the real situation, and improve the accuracy and robustness of the fault diagnosis model, different types of variables are generated in different ways, including: floating-point, integer, categorical, and hierarchical. For effectively considering the class imbalance problem, proper modifications are made to the GAN model, where a normal class discriminator is added. A practical case study concerning the braking system of a high-speed train is carried out to verify the effectiveness of the proposed framework. Compared to the classic GAN, the proposed framework achieves better results with respect to F-measure and G-mean metrics.

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Surge detection for smart grid power distribution using a regression-based signal processing model

Baskar

Dhote²,

Dhote

et al. 2022

Computers and Electrical Engineering

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Deep Learning with Long Short-Term Memory Networks for Diagnosing Faults in Smart Grids

Cited by 5 publications

References 47 publications

Multi–Output Classification Based on Convolutional Neural Network Model for Untrained Compound Fault Diagnosis of Rotor Systems with Non–Contact Sensors

Multi–Output Classification Based on Convolutional Neural Network Model for Untrained Compound Fault Diagnosis of Rotor Systems with Non–Contact Sensors

A Modified Generative Adversarial Network for Fault Diagnosis in High-Speed Train Components with Imbalanced and Heterogeneous Monitoring Data

Surge detection for smart grid power distribution using a regression-based signal processing model

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