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Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest
2018
Sensors
Preserved Fulltext
Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. ...
The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector ...
Diagnosis Results with the Origin Feature Space
Diagnosis Results with the Optimal Feature Space Using the random forest model based on WTFER feature space in Section 5.1 and the optimization flow of ...
doi:10.3390/s18041221
pmid:29659548
pmcid:PMC5948935
fatcat:lx3bjv27krbojde42u4wkklq6e
High-Voltage Circuit Breaker Fault Diagnosis Using a Hybrid Feature Transformation Approach Based on Random Forest and Stacked Auto-Encoder
2018
IEEE transactions on industrial electronics (1982. Print)
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First, we introduce a nonlinear feature mapping in the wavelet package timefrequency energy rate feature space based on random forest binary coding to extend the feature width. ...
Index Terms-Fault diagnosis, feature transformation, high-voltage circuit breaker (HVCB), random forest (RF), stacked autoencoder (SAE), wavelet packet decomposition. 0278-0046 ...
Fig. 5 . 5 Feature space transformation based on RF. (a) CART, (b) Random Forest Classification, (c) Feature space transform based on Random Forest. ...
doi:10.1109/tie.2018.2879308
fatcat:ltprjmmqp5dppgfn5psd7o5pe4
Fault Diagnosis of High-Voltage Circuit Breakers using Mechanism Action Time and Hybrid Classifier
2019
IEEE Access
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Accurate fault diagnosis of high-voltage circuit breakers is crucial for the safety of power grids. ...
The hybrid classifier can not only classify known mechanical states but also detect unknown mechanical faults of high-voltage circuit breakers. ...
In the intelligent diagnosis of HVCBs, feature extraction often consumes a lot of time, especially when signal decomposition is required. ...
doi:10.1109/access.2019.2926100
fatcat:iju2ihhetvbbveqgeud3qj35e4
Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery
[article]
2022
arXiv
pre-print
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As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing with real-world circumstances. ...
Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. ...
There are two main approaches for coping with fault detection and diagnosis in rotating machinery: (1) physical-based control systems and (2) data-driven-based models. ...
arXiv:2202.04212v1
fatcat:bpwtxzxc3rbxhpwg7oiwhb7bvy
Fault Identification of UPFC-Compensated Transmission Lines in Complex Microgrids Using an Intelligent Relaying Scheme Based on Discrete Wavelet Transform and ANN Classifier
[article]
2018
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The study uses discrete wavelet transform (DWT) for feature extraction and artificial neuron network (ANN) for feature classification of fault currents. ...
The main objectives are automatic detection and identification of fault type with the best accuracy, reliability, and reduced computational complexity. ...
In [62] , the authors have used HHT to analyze mechanical vibration signals of high-voltage circuit breakers (HVCB) in order to improve the performance of conventional methods of fault diagnosis. ...
doi:10.11575/prism/32686
fatcat:otpu73n23zhbzevhwqr7vdkkyq