Online railway wheel defect detection under varying running-speed conditions by multi-kernel relevance vector machine

Yi-Qing Ni,Yuan-Hao Wei,You-Wu Wang 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.30 No.3

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

RVM을 이용한 전동기 결함진단

박진희(Jin-Hee Park),권도운(Do-Woon Kwon),양보석(Bo-Suk Yang),이연원(Yeon-Won Lee) 한국소음진동공학회 2012 한국소음진동공학회 학술대회논문집 Vol.2012 No.10

Self-validating Pneumatic Actuator Fault Diagnosis Based on Relevance Vector Machine

Zhigang Feng,Ru Wang 보안공학연구지원센터 2014 International Journal of Control and Automation Vol.7 No.11

In order to solve the fault diagnosis problem of self-validating (SEVA) pneumatic actuator, an actuator fault diagnosis approach based on relevance vector machine (RVM) regression modeling and relevance vector machine (RVM) multi-classifier is proposed. The RVM regression is used to establish the normal models of the SEVA pneumatic actuator. The residuals generated by comparing the output of the models and the actual SEVA actuator are used as the nonlinear features. Then, the structure of the RVM for multi-classification is designed using k-meaning clustering methods, which is used as fault classifier to identify the condition and fault pattern of the SEVA actuator. The proposed approach is verified using fault data generated by DABLib model and actuator data from Lublin Sugar Factory and compared with support vector machine (SVM) fault diagnosis approach. The results indicate that the proposed approach overcomes the drawbacks of SVM and resolves the small sample and nonlinear problem in SEVA pneumatic actuator fault diagnosis.

Machine Learning for Wideband Localization

Thang Van Nguyen,Youngmin Jeong,Hyundong Shin,Win, Moe Z. IEEE 2015 IEEE journal on selected areas in communications Vol.33 No.7

<P>Wireless localization has a great importance in a variety of areas including commercial, service, and military positioning and tracking systems. In harsh indoor environments, it is hard to localize an agent with high accuracy due to non-line-of-sight (NLOS) radio blockage or insufficient information from anchors. Therefore, NLOS identification and mitigation are highlighted as an effective way to improve the localization accuracy. In this paper, we develop a robust and efficient algorithm to enhance the accuracy for (ultrawide bandwidth) time-of-arrival localization through identifying and mitigating NLOS signals with relevance vector machine (RVM) techniques. We also propose a new localization algorithm, called the two-step iterative (TSI) algorithm, which converges fast with a finite number of iterations. To enhance the localization accuracy as well as expand the coverage of a localizable area, we continue to exploit the benefits of RVM in both classification and regression for cooperative localization by extending the TSI algorithm to a centralized cooperation case. For self-localization setting, we then develop a distributed cooperative algorithm based on variational Bayesian inference to simplify message representations on factor graphs and reduce communication overheads between agents. In particular, we build a refined version of Gaussian variational message passing to reduce the computational complexity while maintaining the localization accuracy. Finally, we introduce the notion of a stochastic localization network to verify proposed cooperative localization algorithms.</P>

Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit

Gomathy, V.,Selvaperumal, S. The Korean Institute of Power Electronics 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.3

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit

V. Gomathy,S. Selvaperumal 전력전자학회 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.3

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.