Fault diagnosis approach of traction transformers in high‐speed railway combining kernel principal component analysis with random forest

Dai, Chenxi; Liu, Zhigang; Hu, Keting; Ke, Huang

doi:10.1049/iet-est.2015.0018

Cited by 48 publications

(24 citation statements)

References 14 publications

(19 reference statements)

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“…The RFs model {h 1 (X), h 2 (X), …, h N (X)} can be obtained from multiple decision trees model by training N times. The final classification result is determined by a simple majority vote, which can refer to [36] and [37], and its expression can be elaborated as…”

Section: Training and Evaluation Of Fault Diagnosis Classifiermentioning

confidence: 99%

Fault diagnosis for open‐circuit faults in NPC inverter based on knowledge‐driven and data‐driven approaches

Kou

Liu

Cai

et al. 2020

IET Power Electronics

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In this study, the open-circuit faults diagnosis and location issue of the neutral-point-clamped (NPC) inverters are analysed. A novel fault diagnosis approach based on knowledge driven and data driven was presented for the open-circuit faults in insulated-gate bipolar transistors (IGBTs) of NPC inverter, and Concordia transform (knowledge driven) and random forests (RFs) technique (data driven) are employed to improve the robustness performance of the fault diagnosis classifier. First, the fault feature data of AC in either normal state or open-circuit faults states of NPC inverter are analysed and extracted. Second, the Concordia transform is used to process the fault samples, and it has been verified that the slopes of current trajectories are not affected by different loads in this study, which can help the proposed method to reduce overdependence on fault data. Moreover, then the transformed fault samples are adopted to train the RFs fault diagnosis classifier, and the fault diagnosis results show that the classification accuracy and robustness performance of the fault diagnosis classifier are improved. Finally, the diagnosis results of online fault diagnosis experiments show that the proposed classifier can locate the open-circuit fault of IGBTs in NPC inverter under the conditions of different loads.

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Section: Training and Evaluation Of Fault Diagnosis Classifiermentioning

confidence: 99%

Fault diagnosis for open‐circuit faults in NPC inverter based on knowledge‐driven and data‐driven approaches

Kou

Liu

Cai

et al. 2020

IET Power Electronics

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“…Knowledge-based methods are also used to detect transformer internal faults. Based on the DGA, the kernel principal component analysis (KPCA) combined with random forest was applied to diagnose the traction transformer thermal and electrical faults, which achieved higher diagnosis accuracy than some traditional methods and show good anti-jamming performance [11]. The FDS is also shown to be an effective approach to diagnose the status of the oil-paper with nonuniform aging [12].…”

Section: B Traction Transformer Fault Diagnosismentioning

confidence: 99%

Advances in Fault Diagnosis for High-Speed Railway: A Review

Yin

et al. 2019

2019 6th International Conference on Frontiers of Industrial Engineering (ICFIE)

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The high speed railway (HSR) is a complex system with many subsystems and components. The reliability of its core subsystems is a key consideration in ensuring the safety and operation efficiency of the whole system. As the service time increases, the degradation of these subsystems and components may lead to a range of faults and deteriorate the whole system performance. To ensure the operation safety and to develop reasonable maintenance strategies, fault detection and isolation is an indispensable functionality in high speed railway systems. In this paper, the traction power supply system, bogie system, civil infrastructure system, and control and signaling system of HSR are briefly summarized, and then different fault diagnosis methods for these subsystems are comprehensively reviewed. Finally, some future research topics are discussed.Keywords-High-speed railway (HSR); fault diagnosis; traction power supply system (TPSS); bogie system I.

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“…Second, random forest is suitable for handling large data due to its parallelization [28]. It has been combined with the Spark [28], heuristic bootstrap sampling method [29], kernel principal component analysis [30], and other technologies to perform fault diagnosis and regression tasks [31,32]. Owing to the improvement of the forecasting accuracy for highdimensional and large-scale wind turbine data, we propose an optimized random forest method which consists of a dimension reduction procedure and the weighted voting process for the short-term WPF.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

Sun

Zhang

2018

Mathematical Problems in Engineering

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A variety of supervised learning methods using numerical weather prediction (NWP) data have been exploited for short-term wind power forecasting (WPF). However, the NWP data may not be available enough due to its uncertainties on initial atmospheric conditions. Thus, this study proposes a novel hybrid intelligent method to improve existing forecasting models such as random forest (RF) and artificial neural networks, for higher accuracy. First, the proposed method develops the predictive deep belief network (DBN) to perform short-term wind speed prediction (WSP). Then, the WSP data are transformed into supplementary input features in the prediction process of WPF. Second, owing to its ensemble learning and parallelization, the random forest is used as supervised forecasting model. In addition, a data driven dimension reduction procedure and a weighted voting method are utilized to optimize the random forest algorithm in the training process and the prediction process, respectively. The increasing number of training samples would cause the overfitting problem. Therefore, the k-fold cross validation (CV) technique is adopted to address this issue. Numerical experiments are performed at 15-min, 30-min, 45-min, and 24-h to indicate the superiority and signal advantages compared with existing methods in terms of forecasting accuracy and scalability.

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Fault diagnosis approach of traction transformers in high‐speed railway combining kernel principal component analysis with random forest

Cited by 48 publications

References 14 publications

Fault diagnosis for open‐circuit faults in NPC inverter based on knowledge‐driven and data‐driven approaches

Fault diagnosis for open‐circuit faults in NPC inverter based on knowledge‐driven and data‐driven approaches

Advances in Fault Diagnosis for High-Speed Railway: A Review

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

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