Based on the analysis of shortcomings of commonly used mechanical equipment fault location methods, the paper brought out the method of applying Genetic Algorithm (GA) to solve fault probability of constitute components of mechanical equipments. Aiming at problems of easily local optimum and slow evolution that inherent by standard GA, the paper introduced energy entropy and pseudo-gradient into annealing selection and neighborhood search of GA, so as to take full advantage of effective information of current population and systems information to speed calculation. The material fault location example of some type mechanical equipment verifies that the improved GA can effectively solve the fault location problem of large scale mechanical equipments, the global optimization performance of which is superior to standard GA.
['Zhi-yong Huang']
Research on Fault Location of Large-Scale Mechanical Equipment Based on Improved Genetic Algorithm