An improved social mimic optimization algorithm and its application in bearing fault diagnosis

As a key component of rotating machinery, it is of great significance for the timely diagnosis of bearing weak faults. Stochastic resonance is widely used for its special signal enhancement pattern, and the combination of system parameters determines its actual output effect. Because the social mimic optimization algorithm has the advantages of few parameters, fast convergence speed and strong exploitation capability, it is used to optimize the parameters of stochastic resonance system in this paper. Aiming at the problem that it is easy to fall into local optimum during optimization, inspired by the learning habits of elite, an elite social mimic optimization (ESMO) algorithm is proposed. Its improvement mainly includes five parts: integration of learning efficiency, exchange learning, looking for successors, innovation and breakthrough, elimination mechanism. And its superiority is verified by the comparison of 29 standard benchmark functions and 10 other well-known optimization algorithms. Aiming at the discontinuity of optimization space, the concept of survival rate (surr) is proposed, and the effectiveness of the ESMO algorithm in optimizing discontinuous variables is analyzed and verified. Aiming at the disadvantage that stochastic resonance can only process small frequency signals, a bearing weak fault diagnosis method based on frequency exchange and parameter compensation stochastic resonance is proposed. To verify its application ability in other fault diagnosis methods, a classification and recognition method based on BP neural network is proposed. Finally, the effectiveness and superiority of the ESMO algorithm are verified by simulation signals and bearing experimental data. The above analysis results prove that the ESMO algorithm has certain scientific and engineering application value in the optimization field and engineering application.