Improved PSO based K-Means Clustering Applied to Fault Signals Diagnosis

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

doi:10.1109/cadiag.2018.8751319

Cited by 5 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The traditional K-Means clustering algorithm does not guarantee that the global optimal solution can be obtained, and the clustering effect depends on the selection of initial clustering centers. Therefore, many studies [19]- [22], [33], [34] have focused on optimizing the selection of initial clustering centers of K-Means. In this paper, ACO algorithm is used to get the global optimal initial clustering centers of K-Means.…”

Section: ) Aco-k-means Clustering Algorithmmentioning

confidence: 99%

“…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%

See 3 more Smart Citations

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

Wan

Zhang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: ) Aco-k-means Clustering Algorithmmentioning

confidence: 99%

Section: F Comparison With Other Swarm Intelligence Optimization Algmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Wan

Zhang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…An Ant Lion-based Random Walk Differential Evolution algorithm for optimization and clustering was proposed in [23]. An improved Particle Swarm Optimization (PSO) based K-means applied to Fault signal diagnosis is cited in [24]. A review on clustering with Genetic Algorithms (GA) was done [25].…”

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

View full text Add to dashboard Cite

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),

show abstract

An Improved Jaccard Coefficient‐Based Clustering Approach with Application to Diagnosis and RUL Estimation

Li,

Tang,

Wang

et al. 2024

IET Signal Processing

View full text Add to dashboard Cite

Sample clustering techniques play a crucial role in the data‐driven state evaluation of electromechanical equipment, and selecting an appropriate similarity measurement method for sample sets helps improve the clustering performance. The Jaccard coefficient is a commonly employed indicator of similarity for scalar set‐type samples. In this paper, we propose an incremental clustering algorithm for matrix‐type samples by defining an improved Jaccard coefficient. First, a new binary relation is formulated to derive a relationship matrix between samples. Second, an undirected graph is given by using the relationship matrix, and an improved pruning operation is provided to simplify the graph by eliminating redundant edges. Then, a new relationship matrix is generated according to the modified graph, which enables the calculation of the improved Jaccard coefficient. By using the improved Jaccard coefficient, the improved incremental clustering algorithm updates cluster centers by selecting a particular sample to maximize the sum of similarities between the selected sample and other samples within the same cluster. Finally, the effectiveness of the proposed incremental clustering algorithm is demonstrated in fault diagnosis and remaining useful life estimation application scenarios, respectively. The experimental results indicate that the improved algorithm outperforms traditional clustering methods.

show abstract

Improved PSO based K-Means Clustering Applied to Fault Signals Diagnosis

Cited by 5 publications

References 14 publications

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

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

An Improved Jaccard Coefficient‐Based Clustering Approach with Application to Diagnosis and RUL Estimation

Contact Info

Product

Resources

About