Engine Fault Signals Diagnosis using Genetic Algorithm and K-means based Clustering

Mjahed, Soukaina; Hadaj, Salah El; Bouzaachane, Khadija; Raghay, Said

doi:10.1145/3230905.3230952

Cited by 5 publications

(8 citation statements)

References 22 publications

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“…An unsupervised algorithm can also be used for defect classification. Based on K-means clustering, Mjahed et al [160] presented an efficient algorithm for solving a multiobjective fault signal diagnosis problem using a genetic algorithm. Hamdi et al [161] introduced an unsupervised defect detection algorithm for patterned fabrics.…”

Section: Classificationmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

274

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Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

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Section: Classificationmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

274

View full text Add to dashboard Cite

show abstract

“…To better evaluate the effectiveness of the proposed ACO-K-Means clustering algorithm, the other two different swarm intelligence optimization algorithms including GA [21] and PSO algorithm [22] are also used for optimizing the selection of initial clustering centers of K-Means. Similar to Spark-ACO-K-Means, Spark-based parallel GA-K-Means clustering algorithm (Spark-GA-K-Means) and Spark-based parallel PSO-K-Means clustering algorithm (Spark-PSO-K-Means) are implemented, and the weighted Euclidean distance measure is also used in Spark-GA-K-Means and Spark-PSO-K-Means.…”

Section: F Comparison With Other Swarm Intelligence Optimization Algmentioning

confidence: 99%

“…Zhang et al [20] improved the choice method of initial clustering centers of K-Means, and the results show that the fault diagnosis accuracy of rolling bearing obtained using the modified K-Means clustering algorithm is increased by 7.5% than that obtained using the traditional K-Means clustering algorithm. Soukaina et al [21] proposed an engine fault diagnosis method based on genetic algorithm (GA) and K-Means clustering algorithm, and a novel engine fault diagnosis method based on particle swarm optimization (PSO) algorithm and K-Means clustering algorithm was devised in [22], which can effectively identify different kinds of faults in engines. In [21], [22], both GA and PSO algorithm are exploited to improve the random initialization of K-Means clustering algorithm for engine fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Soukaina et al [21] proposed an engine fault diagnosis method based on genetic algorithm (GA) and K-Means clustering algorithm, and a novel engine fault diagnosis method based on particle swarm optimization (PSO) algorithm and K-Means clustering algorithm was devised in [22], which can effectively identify different kinds of faults in engines. In [21], [22], both GA and PSO algorithm are exploited to improve the random initialization of K-Means clustering algorithm for engine fault diagnosis. Compared with FCM clustering algorithm, K-Means clustering algorithm has lower computational complexity and faster convergence speed, thus it is more suitable for the clustering analysis of big data.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Bearing Fault Diagnosis Method Using Spark-Based Parallel ACO-K-Means Clustering Algorithm

Wan

Zhang

et al. 2021

IEEE Access

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K-Means clustering algorithm is a typical unsupervised learning method, and it has been widely used in the field of fault diagnosis. However, the traditional serial K-Means clustering algorithm is difficult to efficiently and accurately perform clustering analysis on the massive running-state monitoring data of rolling bearing. Therefore, a novel fault diagnosis method of rolling bearing using Spark-based parallel ant colony optimization (ACO)-K-Means clustering algorithm is proposed. Firstly, a Spark-based three-layer wavelet packet decomposition approach is developed to efficiently preprocess the running-state monitoring data to obtain eigenvectors, which are stored in Hadoop Distributed File System (HDFS) and served as the input of ACO-K-Means clustering algorithm. Secondly, ACO-K-Means clustering algorithm suitable for rolling bearing fault diagnosis is proposed to improve the diagnosis accuracy. ACO algorithm is adopted to obtain the global optimal initial clustering centers of K-Means from all eigenvectors, and the K-Means clustering algorithm based on weighted Euclidean distance is used to perform clustering analysis on all eigenvectors to obtain a rolling bearing fault diagnosis model. Thirdly, the efficient parallelization of ACO-K-Means clustering algorithm is implemented on a Spark platform, which can make full use of the computing resources of a cluster to efficiently process large-scale rolling bearing datasets in parallel. Extensive experiments are conducted to verify the effectiveness of the proposed fault diagnosis method. Experimental results show that the proposed method can not only achieve good fault diagnosis accuracy but also provide high model training efficiency and fault diagnosis efficiency in a big data environment.

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“…A review on clustering with Genetic Algorithms (GA) was done [25]. An engine fault signals diagnosis using Genetic Algorithm and K-means based clustering is defined in [26]. A Whale Optimization Algorithm (WOA) approach for clustering is introduced in [27].…”

Section: Introductionmentioning

confidence: 99%

Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

SoukainaMjahed¹,

Bouzaachane²,

Azar³

et al. 2020

Computer Modeling in Engineering &Amp; Sciences

Self Cite

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This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the "Higgs machine learning challenge 2014" data set. This unsupervised detection goes in this paper analysis through 4 steps: (1) selection of the most informative features from the considered data; (2) definition of the number of clusters based on the elbow criterion. The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters; (3) proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization (ALO); (4) comparison with some existing metaheuristic optimizations such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). By employing a multi-angle analysis based on the cluster validation indices, the confusion matrix, the efficiencies and purities rates, the average cost variation, the computational time and the Sammon mapping visualization, the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized with ALO (ALOGK) to validate the proposed approach. Even if the paper gives a complete clustering analysis, its novel contribution concerns only the Steps (1) and (3) considered above. The first contribution lies in the method used for Step (1) to select the most informative features and variables. We used the t-Statistic technique to rank them. Afterwards, a feature mapping is applied using Self-Organizing Map (SOM) to identify the level of correlation between them. Then, Particle Swarm Optimization (PSO), a metaheuristic optimization technique, is used to reduce the data set dimension. The second contribution of this work concern the third step, where each one of the clustering algorithms as K-means (KM), Global K-means (GlobalKM), Partitioning Around Medoids (PAM), Fuzzy C-means (FCM),

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Engine Fault Signals Diagnosis using Genetic Algorithm and K-means based Clustering

Cited by 5 publications

References 22 publications

State of the Art in Defect Detection Based on Machine Vision

State of the Art in Defect Detection Based on Machine Vision

A Novel Bearing Fault Diagnosis Method Using Spark-Based Parallel ACO-K-Means Clustering Algorithm

Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

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