Induction Motor Fault Classification Based on Combined Genetic Algorithm with Symmetrical Uncertainty Method for Feature Selection Task

Lee, Chun‐Yao; Hsieh, Yun-Jhan; Le, Truong-An

doi:10.3390/math10020230

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…In hybrid methods based on filters and optimization methods, the dataset is filtered, and the optimization algorithm iteratively chooses a subset of gene features from the chosen subset until optimum classification accuracy is achieved. Such different algorithms are the brainstorming optimization algorithm [31], Particle Swarm Optimization (PSO) [32], Whale Optimization [33], Genetic Algorithm (GA) [34], and Moth Flam Optimization [35]. AOA was recently suggested by [30] and uses arithmetic operators, including multiplication and division, for exploring the search space and addition and subtraction operators for exploiting the search space, to discover the best solution for a given problem.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Alsmadi

2024

CMC

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Lung cancer is among the most frequent cancers in the world, with over one million deaths per year. Classification is required for lung cancer diagnosis and therapy to be effective, accurate, and reliable. Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner. Machine Learning (ML) has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique. Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification. Normally, microarrays include several genes and may cause confusion or false prediction. Therefore, the Arithmetic Optimization Algorithm (AOA) is used to identify the optimal gene subset to reduce the number of selected genes. Which can allow the classifiers to yield the best performance for lung cancer classification. In addition, we proposed a modified version of AOA which can work effectively on the high dimensional dataset. In the modified AOA, the features are ranked by their weights and are used to initialize the AOA population. The exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood strategies. Finally, the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold cross-validation. The method's efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10% to 25%. Moreover, the approach significantly enhances lung cancer prediction accuracy. For instance, Lung_Harvard1 achieved an accuracy of 97.5%, Lung_Harvard2 and Lung_Michigan datasets both achieved 100%, Lung_Adenocarcinoma obtained an accuracy of 88.2%, and Lung_Ontario achieved an accuracy of 87.5%. In conclusion, the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.

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Section: Introductionmentioning

confidence: 99%

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Alsmadi

2024

CMC

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“…For this purpose, the researchers proposed to use feature selection algorithms before the classification stage. 15–17 Feature selection, as a dimensionality reduction techniques, such as a particle swarm optimization, 18 Genetic Algorithm, 19 Marine Predators Algorithm (MPA), 20 Henry Gas Solubility Optimization (HGSO), 21 Emperor Penguin Optimizer (EPO), 22 Slime Mould Algorithm (SMA), 23 and Tree Seed Algorithm (TSA). 24 However, these algorithms have some drawbacks and suffering from a high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

New criteria for wrapper feature selection to enhance bearing fault classification

Sahraoui,

Rahmoune,

Meddour

et al. 2023

Advances in Mechanical Engineering

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Classification is a critical task in many fields, including signal processing and data analysis. The accuracy and stability of classification results can be improved by selecting the most relevant features from the data. In this paper, a new criterion for feature selection using wrapper method is proposed, which is based on the evaluation of the classification results according to the accuracy and stability (standard deviation) of each class and the number of selected features. The proposed method is evaluated using Random Forest (RF) and Ant Colony Optimization (ACO) algorithms on a benchmark dataset. Results show that the proposed method outperforms classical feature selection methods in terms of accuracy and stability of classification results, especially for the difficult-to-classify combined damage class. This study demonstrates the effectiveness of the proposed new wrapper feature selection criterion to improve the performance of classification algorithms with higher stability (STD: C1 = 0.5, C2 = 0.8, C3 = 0.6, C4 = 1.8) and better accuracy (average C1 = 98.5%, C2 = 96.6%, C3 = 9.5%, C4 = 93) for the both; the statoric current and the vibration signal compared to other techniques. Machine learning methods had proven their efficiency in time-varying machines fault diagnosis when taking vibration signals and statoric currents extracted features as inputs. However, the use of the both demonstrated a higher robustness and a remarkable superiority.

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“…KNN is one of the simplest machine learning methods for classification problems. These methods have been implemented using both current and vibration signals [22][23][24].…”

mentioning

confidence: 99%

Physical Variable Measurement Techniques for Fault Detection in Electric Motors

et al. 2023

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Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies such as electric vehicles, automatic control, maintenance systems, and the inclusion of renewable energy sources in electrical systems, among others. Hence, monitoring, fault detection, and classification are topics of interest for researchers, given that the presence of a fault can lead to catastrophic consequences concerning technical and financial aspects. To detect a fault in an induction motor, several techniques based on different physical variables, such as vibrations, current signals, stray flux, and thermographic images, have been studied. This paper reviews recent investigations into physical variables, instruments, and techniques used in the analysis of faults in induction motors, aiming to provide an overview on the pros and cons of using a certain type of physical variable for fault detection. A discussion about the detection accuracy and complexity of the signals analysis is presented, comparing the results reported in recent years. This work finds that current and vibration are the most popular signals employed to detect faults in induction motors. However, stray flux signal analysis is presented as a promising alternative to detect faults under certain operating conditions where other methods, such as current analysis, may fail.

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Induction Motor Fault Classification Based on Combined Genetic Algorithm with Symmetrical Uncertainty Method for Feature Selection Task

Cited by 7 publications

References 30 publications

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

New criteria for wrapper feature selection to enhance bearing fault classification

Physical Variable Measurement Techniques for Fault Detection in Electric Motors

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