Generalized fault diagnosis method of transmission lines using transfer learning technique

Shakiba, Fatemeh Mohammadi; Shojaee, Milad; Azizi, S. Mohsen; Zhou, MengChu

doi:10.1016/j.neucom.2022.05.022

Cited by 15 publications

(4 citation statements)

References 40 publications

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“…The confusion matrix of the Res-CBDNN diagnostic model in different noise environments is shown in Figure 13. The model's fault state detection and fault location capabilities are analyzed based on the indicators presented in the literature [34], and the calculation result is shown in Tables 6 and 7.…”

Section: Verification Of Robustness Of the Diagnostic Modelmentioning

confidence: 99%

High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN

Xie,

Shi,

Xue

et al. 2024

JMSE

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The fault detection and diagnosis of a ship’s electric propulsion system is of great significance to the reliability and safety of large modern ships. The traditional fault diagnosis method based on mathematical models and expert knowledge is limited by the difficulty of establishing an accurate model of the complex system, and it is easy to cause false alarms. Data-driven methods, such as deep learning, can automatically learn from the mass of data, extract and analyze fault characteristics, and create a more objective distinction system state. A deep learning fault diagnosis model based on ResNet feature extraction capability and bidirectional long-term memory network timing processing capability is proposed to realize fault diagnosis of high resistance connections in ship electric propulsion systems. The results show that the res-convolutional BiLSTM deep neural network (Res-CBDNN) can fully integrate the advantages of the two networks, efficiently process fault current data, and achieve high-performance fault diagnosis. The accuracy of Res-CBDNN can be kept above 85% in a noisy environment, and it can effectively monitor the high resistance connection fault of ship electric propulsion systems.

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Section: Verification Of Robustness Of the Diagnostic Modelmentioning

confidence: 99%

High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN

Xie,

Shi,

Xue

et al. 2024

JMSE

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“…In another work [33], a CNN and long short-term memory (LSTM) based hybrid model has been used for improving performances in high impedance faults. The fault diagnosis efficiency in diverse length transmission lines has been improved by using a transfer learning framework based on a pre-trained LeNet-5 CNN, as addressed in [2]. Furthermore, YOLOv5 deep learning algorithm has been employed in [34] to improve TLs fault identification accuracy in complicated backgrounds.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Due to the significance of TLs, their failures can severely affect the stability and uninterrupted operations of the power grids. Extreme atmospheric and climatic conditions, including lightning, ice, tree interference, bird nesting, breaking, hurricanes, aging, human activities, and a lack of preservation, are some of the notable causes for the failures of TLs, as discussed in [2]. Also, faults can be caused by other factors like damaged insulation, faulty equipment, or outside interference.…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of different transmission line fault detectors and classifiers during normal conditions and cyber‐attacks

Tusher,

Rahman,

Islam

et al. 2024

The Journal of Engineering

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Transmission lines, the core part of the transmission and distribution system in the smart grid, require effective, efficient, and reliable protective measures against faults to avoid severe damage to physical infrastructure and financial losses. Due to their growing popularity, machine learning models are used in fault detection and classification, whose performances can be severely affected by cyber‐attacks due to their data dependency, posing a critical concern. Hence, this paper introduces false data injection attacks to address the vulnerability of machine learning‐based fault detectors and classifiers. A comparative study of 9 detection models and 6 classification models under normal conditions and during a combination of two models of false data injection attacks is presented to evaluate the severity of cyber‐attacks. Experimental results show that highly accurate models in normal conditions are more susceptible to cyber‐attacks, with up to 69% and 28% degradations in accuracy for fault detectors and classifiers, respectively. Furthermore, the detection models are found to be more vulnerable to cyber‐attacks than the classification models. With no robust detectors and classifiers being found, this work addresses the importance of developing attack‐resilient fault detection and classification schemes considering their academic and industrial significance.

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“…(2) In real application scenarios, it is necessary to detect and diagnose a massive volume of data, to ensure the smooth operation of systems, including the operation of fault-free data. However, many existing methods [3][4][5] employ a one-stage diagnosis strategy, where the existence and categories of faults are detected simultaneously. This strategy treats normal data and other fault categories as equally important.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

Yang,

Long,

Lin

et al. 2023

Computers, Materials &Amp; Continua

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With the rapid development of the Internet of Things (IoT), the automation of edge-side equipment has emerged as a significant trend. The existing fault diagnosis methods have the characteristics of heavy computing and storage load, and most of them have computational redundancy, which is not suitable for deployment on edge devices with limited resources and capabilities. This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation. First, we offer a clustering-based self-knowledge distillation approach (Cluster KD), which takes the mean value of the sample diagnosis results, clusters them, and takes the clustering results as the terms of the loss function. It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model, especially for fault categories with high similarity. Then, the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweight model for edge-side deployment. We propose a two-stage edge-side fault diagnosis method (TSM) that separates fault detection and fault diagnosis into different stages: in the first stage, a fault detection model based on a denoising auto-encoder (DAE) is adopted to achieve fast fault responses; in the second stage, a diverse convolution model with variance weighting (DCMVW) is used to diagnose faults in detail, extracting features from micro and macro perspectives. Through comparison experiments conducted on two fault datasets, it is proven that the proposed method has high accuracy, low delays, and small computation, which is suitable for intelligent edge-side fault diagnosis. In addition, experiments show that our approach has a smooth training process and good balance.

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Generalized fault diagnosis method of transmission lines using transfer learning technique

Cited by 15 publications

References 40 publications

High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN

High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN

A comparative analysis of different transmission line fault detectors and classifiers during normal conditions and cyber‐attacks

Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

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