A study on reliability centered maintenance planning of a standard electric motor unit subsystem using computational techniques †
AbstractThe design and manufacture of urban transportation applications has been necessarily complicated in order to improve its safety. Urban transportation systems have complex structures that consist of various electric, electronic, and mechanical components, and the maintenance costs generally take up approximately 60% of the total operational costs. Therefore, it is essential to establish a maintenance plan that takes into account both safety and cost. In considering safety and cost limitations, this research introduces an advanced reliability centered maintenance (RCM) planning method using computational techniques, and applies the method to a standard electric motor unit (EMU) subsystem. First, this research devises a maintenance cost function that can reflect the current operating conditions, and maintenance characteristics, of components by generating essential cost factors. Second, a reliability growth analysis (RGA) is performed, using the Army Material Systems Analysis Activity (AMSAA) model, to estimate reliability indexes such as failure rate, and mean time between failures (MTBF), of a standard EMU subsystem, and each individual component Third, two optimization processes are performed to ascertain the optimal maintenance reliability of each component in the standard EMU subsystem. Finally, this research presents the maintenance time of each component based on the optimal maintenance reliability provided by optimization processesand reliability indexes provided by the RGA method.
Urban transit is a complex system that contains both electrical and mechanical entities; therefore, it is necessary to construct a maintenance system for ensuring safety during high-speed driving. Expert systems are computer programs that use numerical or non-numerical domain-specific knowledge to solve problems. This research aims to develop an expert system that diagnoses the causes of failures quickly and displays measures to correct them. For the development of this expert system, the standardization of a failure code classification and the creation of a Bill of Materials (BOM) were first performed. Through the analysis of both failure history and maintenance manuals, a knowledge base has been constructed. Also, for retrieving the procedure of failure diagnosis and repair linking with the knowledge base, we have built a Rule-Based Reasoning (RRB) engine with a pattern matching technique and a Case-Based Reasoning (CBR) engine with a similar search method. Finally, this system has been developed as web based in order to maximize accessibility.
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