Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network

Li, Zhenhua; Zhang, Yujie; Abu-Siada, A.; Chen, Xingxin; Liu, Zhenxing; Xu, Yanchun; Zhang, Lei; Tong, Yue

doi:10.3390/en14061531

Cited by 32 publications

(19 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A key parameter for the DT is to set proper classification variables and classification thresholds for the node(s) of each layer of the tree structure. The values for the higher nodes' classification variables and thresholds determine the homogeneity and heterogeneity among the nodes in the lower layers [26]. In the case that the target variable is discrete, characteristic values such as the pvalue of the chi-square statistic, Gini coefficient, and entropy index can be used for the classification thresholds in DT.…”

Section: Decision Treementioning

confidence: 99%

“…It also works for both numerical and categorical data and has a simple formula, and thus can process massive data in a relatively short time. However, the pruning process for avoiding the over-fitting and under-fitting problems in DT should be based on experience and, even in the cases of having proper values for pruning, the complete resolution of these problems is not guaranteed [25,26].…”

Section: Decision Treementioning

confidence: 99%

See 1 more Smart Citation

FMECA and MFCC-Based Early Wear Detection in Gear Pumps in Cost-Aware Monitoring Systems

2021

View full text Add to dashboard Cite

Gear pump failures in industrial settings are common due to their exposure to uneven high-pressure outputs within short time periods of machine operation and uncertainty. Improving the field and line clam are considered as the solutions for these failures, yet they are quite insufficient for optimal reliability. This research, therefore, suggests a method for early wear detection in gear pumps following an extensive failure modes, effects, and criticality analysis (FMECA) of an AP3.5/100 external gear pump manufactured by BESCO. To replicate this condition, fine particles of iron oxide (Fe2O3) were mixed with the experimental fluid, and the resulting vibration data were collected, processed, and exploited for wear detection. The intelligent wear detection process was explored using various machine learning algorithms following a mel-frequency cepstral coefficient (MFCC)-based discriminative feature extraction process. Among these algorithms, extensive performance evaluation reveals that the random forest classifier returned the highest test accuracy of 95.17%, while the k-nearest neighbour was the most cost efficient following cross validations. This study is expected to contribute to improved evaluations of gear pump failure diagnosis and prognostics.

show abstract

Section: Decision Treementioning

confidence: 99%

Section: Decision Treementioning

confidence: 99%

FMECA and MFCC-Based Early Wear Detection in Gear Pumps in Cost-Aware Monitoring Systems

2021

View full text Add to dashboard Cite

show abstract

“…To make the fuzzy similarity matrix R transitive, when the transfer packet t(R) = R 2K has a natural positive integer K such that R 2K = R 2(K+1) , t(R) is the corresponding fuzzy equivalent matrix R * . The transfer packet algorithm is shown in (14).…”

Section: ) Fuzzy Equivalence Matrix R *mentioning

confidence: 99%

“…In terms of the transformer's state diagnosis algorithm, Zhi et al [14] proposed establishing the transformer fault diagnosis model using a neural network and decision tree. However, due to the limited real data of transformer fault samples and the need to train the diagnosis algorithm in advance, there were some problems, including "poor" practicability and difficult selection of training parameters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering

Zhu¹,

Dong

2021

IEEE Access

View full text Add to dashboard Cite

The onboard traction transformer is a critical equipment of high-speed trains, its running state directly affects the safety and stability of a train's operation. Given the complexity of the running condition of the onboard traction transformer, this paper proposes a running state diagnosis algorithm based on kernel principal component analysis (KPCA) and fuzzy clustering. To fully extract the status information of the onboard traction transformer, the aging characteristics of insulating oil and main insulation are analyzed under different running mileage as the first step. Thereby, to eliminate the signal redundancy, the status feature set of the onboard traction transformer is analyzed by KPCA combined with the characteristic quantities of the traditional dissolved gas analysis (DGA), and the eigenvalues with the contribution rate of over 95% are used as new eigenvectors. Finally, a status diagnosis model is established by using fuzzy clustering analysis, considering the limitations of fault data of onboard traction transformer. The results from field collected data show that the proposed method is effective in diagnosing the running status of the onboard traction transformer.INDEX TERMS Onboard traction transformer; running status diagnosis; insulation aging; kernel principal component analysis; fuzzy clustering

show abstract

Predicting GPR40 Agonists with A Deep Learning‐Based Ensemble Model

et al. 2023

View full text Add to dashboard Cite

Recent studies have identified G protein‐coupled receptor 40 (GPR40) as a promising target for treating type 2 diabetes mellitus, and GPR40 agonists have several superior effects over other hypoglycemic drugs, including cardiovascular protection and suppression of glucagon levels. In this study, we constructed an up‐to‐date GPR40 ligand dataset for training models and performed a systematic optimization of the ensemble model, resulting in a powerful ensemble model (ROC AUC: 0.9496) for distinguishing GPR40 agonists and non‐agonists. The ensemble model is divided into three layers, and the optimization process is carried out in each layer. We believe that these results will prove helpful for both the development of GPR40 agonists and ensemble models. All the data and models are available on GitHub. (https://github.com/Jiamin‐Yang/ensemble_model)

show abstract

Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network

Cited by 32 publications

References 25 publications

FMECA and MFCC-Based Early Wear Detection in Gear Pumps in Cost-Aware Monitoring Systems

FMECA and MFCC-Based Early Wear Detection in Gear Pumps in Cost-Aware Monitoring Systems

Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering

Predicting GPR40 Agonists with A Deep Learning‐Based Ensemble Model

Contact Info

Product

Resources

About