Ling Wang

Purpose: The purpose of this paper is to propose an availability modeling method of complex multiple units system (CMUS) based on the multi-agent technique. Design/methodology/approach: Based on the multi-agent technique, this paper describes the availability model structure for CMUS and develops agent-based models of components, maintenance policies, maintenance tools, maintenance fields, and maintenance staff, as well as the communication method among the different agents. On the basis of the agent-based availability modeling theory, the availability simulation scheme of CMUS is given using MATLAB. Thus, the availability modeling theory of CMUS and its simulation method are developed. To demonstrate the applicability of the proposed availability modeling method, a numerical example is given. Findings: The proposed agent-based modeling method is applicable to availability modeling of CMUS, including the modeling of component failure, maintenance tools/fields/staff, maintenance policy, and structural/economic dependence among components. Practical implications: As a bottom-top, modular, expandable, and reusable modeling theory, the agent-based modeling method might be useful for availability modeling of different CMUSs in reality. Originality/value: The multi-agent technique is introduced into availability modeling of multi-component systems in this paper. Thus, it is possible to model failure of many components, maintenance policies, maintenance tools, maintenance fields, and maintenance staff together for availability analysis of complex systems of equipment.

In most railway companies in China, the loss of nuts from rail fasteners is still found by visual inspections performed by maintenance workers; this approach cannot meet the continuing requirements to reduce maintenance costs and improve maintenance efficiency. In this paper, a computer vision system for detecting the loss of nuts from rail fasteners is proposed that is based on kernel two-dimensional principal component – two-dimensional principal component analysis (K2DPCA-2DPCA) and a support vector machine (SVM). The framework of this information-technology-based system is introduced, and the calculation method to find the positions where nuts are missing is also proposed. The K2DPCA-2DPCA feature extraction method and SVM used for feature classification in the computer vision system are described in detail. Finally, the combination of K2DPCA-2DPCA and SVM is compared with other feature extraction and feature classification methods in an experiment on the detection of the loss of nuts from rail fasteners. In this paper, it is found that the algorithm based on K2DPCA-2DPCA and a SVM is a better method for the computer vision system. It can identify the loss of nuts from a rail fastener with a high recognition rate and reach the maximum recognition rate using only a relatively small number of features. Furthermore, for the same number of training samples, the algorithm based on K2DPCA-2DPCA and SVM is also better than other algorithms for use in the computer vision system.