Decision Tree Method for Fault Causes Classification Based on RMS-DWT Analysis in 275 kV Transmission Lines Network

Asman, Saidatul Habsah; Aziz, Nur Fadilah Ab; Amirulddin, Ungku Anisa Ungku; Kadir, Mohd Zainal Abidin Ab

doi:10.3390/app11094031

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…To detect intermittent fault the mean difference and standard error comparison with predefined threshold are used. A statistical method to classify the faults in power transmission lines [28]. The authors used a decision tree method and the measured parameters are the root mean square current duration, voltage dip, and discrete wavelet transform.…”

Section: Related Workmentioning

confidence: 99%

Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

et al. 2023

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In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor nodes are becoming faulty and having adverse effects in the network if they are not diagnosed and inform the fault status to other nodes. Fault diagnosis is difficult when the nodes behave faulty some times and provide good data at other times. The intermittent disturbances may be random or kind of spikes either in regular or irregular intervals. In literature, the fault diagnosis algorithms are based on statistical methods using repeated testing or machine learning. To avoid more complex and time consuming repeated test processes and computationally complex machine learning methods, we proposed a one shot likelihood ratio test (LRT) here to determine the fault status of the sensor node. The proposed method measures the statistics of the received data over a certain period of time and then compares the likelihood ratio with the threshold value associated with a certain tolerance limit. The simulation results using a real time data set shows that the new method provides better detection accuracy (DA) with minimum false positive rate (FPR) and false alarm rate (FAR) over the modified three sigma test. LRT based hybrid fault diagnosis method detecting the fault status of a sensor node in wireless sensor network (WSN) for real time measured data with 100% DA, 0% FAR and 0% FPR if the probability of the data from faulty node exceeds 25%.INDEX TERMS Wireless sensor network, intermittent fault, likelihood ratio test, fault diagnosis, distributed algorithm.

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Section: Related Workmentioning

confidence: 99%

Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

et al. 2023

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“…DT is one of the most cost-efficient and reliable ML algorithms and this success is attributed to its tree-based architecture. As Figure 3 illustrates, DT is an algorithm built upon a tree-like structure of decision-making rules, which functions to classify the input data into several subsets and perform predictions based on this classification [25]. 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.…”

Section: Decision Treementioning

confidence: 99%

“…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. In the case of a continuous target variable, the F-value in the analysis of variance (ANOVA) or variance reduction is used for the threshold [25]. Unlike other algorithms, DT is a typical white-box model and it does not hide what is used as the threshold for each node's classification.…”

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%

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

2021

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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.

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“…Time-domain features extracted from fault waveform and time stamp were used to construct logic flow to classify lightning-, animal-and tree-induced faults [6]. To exploit transient characteristics in the frequency domain, signal processing techniques such as wavelet transform (WT) and empirical mode decomposition (EMD) are used for further waveform characteristic analysis [17][18][19][20]. In [21], a fault waveform was characterized based on the time and frequency domain to develop an identification logic.…”

Section: Introductionmentioning

confidence: 99%

Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection

et al. 2021

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Fault-cause identification plays a significant role in transmission line maintenance and fault disposal. With the increasing types of monitoring data, i.e., micrometeorology and geographic information, multiview learning can be used to realize the information fusion for better fault-cause identification. To reduce the redundant information of different types of monitoring data, in this paper, a hierarchical multiview feature selection (HMVFS) method is proposed to address the challenge of combining waveform and contextual fault features. To enhance the discriminant ability of the model, an ε-dragging technique is introduced to enlarge the boundary between different classes. To effectively select the useful feature subset, two regularization terms, namely l2,1-norm and Frobenius norm penalty, are adopted to conduct the hierarchical feature selection for multiview data. Subsequently, an iterative optimization algorithm is developed to solve our proposed method, and its convergence is theoretically proven. Waveform and contextual features are extracted from yield data and used to evaluate the proposed HMVFS. The experimental results demonstrate the effectiveness of the combined used of fault features and reveal the superior performance and application potential of HMVFS.

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Decision Tree Method for Fault Causes Classification Based on RMS-DWT Analysis in 275 kV Transmission Lines Network

Cited by 13 publications

References 17 publications

Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

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

Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection

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