Abstract: Aiming at the problem that the selection and optimization of mechanical fault parameters are neglected
in the current research on mechanical fault diagnosis，which leads to poor accuracy，this paper proposes a sparse
feature similarity measurement method for mechanical fault based on quantum genetic algorithm optimization．
According to the first signal nonlinear mixing，then demixing． With kurtosis as the objective function and
quantum genetic algorithm，the separation matrix parameters and nonlinear de-mixing parameters of blind source
separation are optimized to realize blind source separation of mechanical faults． Based on fault signal processing，
the similarity measurement of mechanical fault sparse features is realized by combining quantum genetic algorithm
with LSSVM ( Least Squares Support Vector Machine)． LSSVM selects the model parameters during the
mechanical fault diagnosis，the quantum genetic algorithm is used to optimize the model parameters of LSSVM．
The parameter selection problem of LSSVM is transformed to an optimization problem，and the optimized LSSVM
classification model is used to realize the classification of mechanical fault sparse feature similarity mode． The
experimental results show that the method can achieve high efficiency blind source separation，high accuracy in
mechanical fault diagnosis and good performance．

Key words: quantum genetic algorithm, mechanical failure, feature, similarity measure