Abstract：In order to solve the problems of high subjective misjudgment rate and low efficiency in manual

hand touch and auscultation methods for acoustic quality detection of micro motors，while taking into account the

accuracy of detection results and the fast construction of detection models，a small sample machine learning

detection method was proposed. Based on the physical model of micro motor transmission chain，multi-dimensional acoustic fault features were extracted，particle swarm optimization was used to optimize the core parameters of support vector machine，a small sample learning method，so as to improve the accuracy of model discrimination.The experimental results show that this method can effectively distinguish abnormal vibration and sound of micro motors，with an accuracy rate of over 95%.

      Key words：micro motor；quality inspection；physical model；particle swarm optimization（PSO）；support

vector machine（SVM）