Assembly fault rate analysis using grey relation and least squares support vector machines
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Abstract:
To get the relationship between assembly fault rate and its attributes, least squares support vector machine (LSSVM)is introduced to quantitatively study assembly fault rate. Aiming at the drawbacks of assembly reliability evaluation method(AREM), the attributes of assembly-fault-rate-affecting 5M1E(Man, Machine, Material, Method, Measurement and Environment) factors obtained by AREM are improved, hence the LSSVM model with all attributes is established. To reduce the time of calculating the assembly fault rate and provide the priority for assembly reliability improvement, grey relation analysis is applied to extracting the main attributes, at the same time genetic algorithm(GA)is used for parameter optimization in LSSVM. The assembly fault rate analysis results show that the method using grey relation analysis and least square support vector machine is simpler and more accurate compared with other methods such as LSSVM model using all attributes and BP neural network using main attributes.
