Fault Diagnosis for Electrical Systems and Power Networks: A Review

Furse, Cynthia; Kafal, Moussa; Razzaghi, Reza; Shin, Yong-June

doi:10.1109/jsen.2020.2987321

Cited by 178 publications

(65 citation statements)

References 158 publications

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“…Reflectometry is a nondestructive technique to remotely detect, locate, and characterize useful information in physical systems. It has been used extensively in electrical systems to locate electrical “opens” or “shorts” [ 1 , 2 , 3 , 4 , 5 , 6 ], locate cable insulation break down [ 7 ], detect electrical system degradation [ 8 , 9 , 10 , 11 ], locate ground and arc faults [ 11 , 12 , 13 , 14 ], locate damaged components in photovoltaic (PV) systems [ 12 , 15 , 16 , 17 , 18 ], remotely measure complex impedance [ 17 , 19 , 20 , 21 , 22 ], locate intermittent faults that only occur during normal operation [ 3 , 4 , 8 , 23 , 24 , 25 , 26 ], and locate faults in three phase systems [ 27 ]. In material science, reflectometry has been used to characterize nanostructures, optical fiber networks, waveguides, and dielectrics [ 28 , 29 , 30 , 31 ].…”

Section: Reflectometrymentioning

confidence: 99%

“…The reflected EM energy echoes back to the test device for evaluation to produce the reflectometry response. The reflectometry response characteristics and its ability to give the expected information are determined by the type of reflectometry method used [ 5 , 6 ].…”

Section: Reflectometrymentioning

confidence: 99%

“…It has natural noise immunity [ 5 ] so experiences minimal interference from existing signals in the system or external electrical noise sources. It possesses a dynamic frequency-domain bandwidth that can be varied by the modulation frequency [ 6 ]. The ability to send signals on energized systems without interfering with them comes from making the amplitude of the PN code ( Figure 2 a,c) very small compared to the native signal in the system under test.…”

Section: Reflectometrymentioning

confidence: 99%

See 2 more Smart Citations

A SSTDR Methodology, Implementations, and Challenges

Kingston

Benoit

Edun

et al. 2021

Sensors

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Sequence time-domain reflectometry (STDR) and spread spectrum time-domain reflectometry (SSTDR) detect, locate, and diagnose faults in live (energized) electrical systems. In this paper, we survey the present SSTDR literature for discussions on theory, algorithms used in its analysis, and its more prominent implementations and applications. Our review includes both scientific litera-ture and selected patents. We also discuss future applications of SSTDR.

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Section: Reflectometrymentioning

confidence: 99%

Section: Reflectometrymentioning

confidence: 99%

Section: Reflectometrymentioning

confidence: 99%

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A SSTDR Methodology, Implementations, and Challenges

Kingston

Benoit

Edun

et al. 2021

Sensors

Self Cite

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“…Bearings are the core components of rotating machinery, therefore, it is of great significance to diagnose the fault of the bearings in time. Since the 1960s, fault diagnosis has gradually attracted scholars’ attention in the field of science and technology [ 1 ]. Traditional fault diagnosis is a diagnosis method based on fault mechanism.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis

Wang

Chen

Liu

et al. 2021

Sensors

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Neural networks for fault diagnosis need enough samples for training, but in practical applications, there are often insufficient samples. In order to solve this problem, we propose a wavelet-prototypical network based on fusion of time and frequency domain (WPNF). The time domain and frequency domain information of the vibration signal can be sent to the model simultaneously to expand the characteristics of the data, a parallel two-channel convolutional structure is proposed to process the information of the signal. After that, a wavelet layer is designed to further extract features. Finally, a prototypical layer is applied to train this network. Experimental results show that the proposed method can accurately identify new classes that have never been used during the training phase when the number of samples in each class is very small, and it is far better than other traditional machine learning models in few-shot scenarios.

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“…This strategy allows operators to estimate the residual lifetime and to schedule predictive maintenance operations [22]. In addition, suitable real-time analysis algorithms are key elements for this purpose [23].…”

Section: Introductionmentioning

confidence: 99%

State of Health Prediction of Power Connectors by Analyzing the Degradation Trajectory of the Electrical Resistance

2021

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Estimating the remaining useful life (RUL) or the state of health (SoH) of electrical components such as power connectors is still a challenging and complex task. Power connectors play a critical role in medium- and high-voltage power networks, their failure leading to important consequences such as power outages, unscheduled downtimes, safety hazards or important economic losses. Online condition monitoring strategies allow developing improved predictive maintenance plans. Due to the development of low-cost sensors and electronic communication systems compatible with Internet of Things (IoT) applications, several methods for online and offline SoH determination of diverse power devices are emerging. This paper presents, analyzes and compares the performance of three simple and effective methods for online determination of the SoH of power connectors with low computational requirements. The proposed approaches are based on monitoring the evolution of the connectors’ electrical resistance, which defines the degradation trajectory because the electrical resistance is a reliable indicator or signature of the SoH of the connectors. The methods analyzed in this paper are validated by means of experimental ageing tests emulating real degradation conditions. Laboratory results prove the suitability and feasibility of the proposed approach, which could be applied to other power products and apparatus.

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Fault Diagnosis for Electrical Systems and Power Networks: A Review

Cited by 178 publications

References 158 publications

A SSTDR Methodology, Implementations, and Challenges

A SSTDR Methodology, Implementations, and Challenges

Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis

State of Health Prediction of Power Connectors by Analyzing the Degradation Trajectory of the Electrical Resistance

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