A K-means clustering method with feature learning for unbalanced vehicle fault diagnosis. Issue 2 (24th June 2021)

• Record Type: Journal Article
• Authors: Wang, Bo; Wang, Guanwei; Wang, Youwei; Lou, Zhengzheng; Hu, Shizhe; Ye, Yangdong
• Is Part Of: Smart and resilient transportation. Volume 3:Issue 2(2022)
• Page Start: 162
• Page End: 176
• Publication Date: 2021-06-24
• Subjects: K-means -- Cluster-centers selection method -- Feature learning K-means -- Unbalanced vehicle faults diagnosis; 629.04
• DOI: 10.1108/SRT-01-2021-0003
• Languages: English
• ISSNs: 2632-0487

Abstract

Abstract : Purpose: Vehicle fault diagnosis is a key factor in ensuring the safe and efficient operation of the railway system. Due to the numerous vehicle categories and different fault mechanisms, there is an unbalanced fault category problem. Most of the current methods to solve this problem have complex algorithm structures, low efficiency and require prior knowledge. This study aims to propose a new method which has a simple structure and does not require any prior knowledge to achieve a fast diagnosis of unbalanced vehicle faults. Design/methodology/approach: This study proposes a novel K-means with feature learning based on the feature learning K-means-improved cluster-centers selection (FKM-ICS) method, which includes the ICS and the FKM. Specifically, this study defines cluster centers approximation to select the initialized cluster centers in the ICS. This study uses improved term frequency-inverse document frequency to measure and adjust the feature word weights in each cluster, retaining the top τ feature words with the highest weight in each cluster and perform the clustering process again in the FKM. With the FKM-ICS method, clustering performance for unbalanced vehicle fault diagnosis can be significantly enhanced. Findings: This study finds that the FKM-ICS can achieve a fast diagnosis of vehicle faults on the vehicle fault text (VFT) data set from a railway station in the 2017 (VFT) data set. The experimental results on VFT indicate the proposed method in this paper, outperforms several state-of-the-art methods. Originality/value: This is the first effort to address the vehicle fault diagnostic problem and the proposed method performs effectively and efficiently. The ICS enables the FKM-ICS method to exclude the effect of outliers, solves the disadvantages of the fault text data contained a certain amount of noisy data, which effectively enhanced the method stability. The FKM enhances the distribution of feature words that discriminate between different fault categories and reduces the number of feature words to make the FKM-ICS method faster and better cluster for unbalanced vehicle fault diagnostic.