基于自适应双稳态随机共振的中介轴承故障诊断方法

针对航空发动机中介轴承故障信号微弱，故障特征提取困难的问题，提出了基于容忍遗传算法（TAGA）的自适应双稳态随机共振（BSR）的中介轴承故障诊断方法。在传统自适应遗传算法中引入容忍度思想，建立一种容忍遗传算法，采用容忍遗传算法对双稳态随机共振系统的结构参数a，b进行优化，建立自适应双稳态随机共振系统对故障信号进行处理。为验证该方法的有效性，搭建了中介轴承故障模拟实验系统，开展中介轴承内圈和外圈故障模拟实验。采用该方法分别对仿真信号和实验信号进行处理。结果表明:该方法能够对故障信号进行增强，提升了故障特征频率提取能力。自适应优化结构参数后，提取的特征频率与故障频率理论值的误差小于0.1%。 

Fault diagnosis method of inter-shaft bearing based on adaptive bistable stochastic resonance

In view of the problem that the aeroengine inter-shaft bearing fault signal is weak and the fault feature extraction is difficult, an adaptive bistable stochastic resonance (BSR) fault diagnosis method based on tolerance genetic algorithm (TAGA) of inter-shaft bearing was proposed. The tolerance theory was introduced into the traditional adaptive genetic algorithm, and a TAGA was established. The structural parameters a and b of the BSR system were optimized by the TAGA. The fault signal was processed by adaptive BSR system. In order to verify the effectiveness of the proposed method, an inter-shaft bearing fault simulation test rig was built, and the inner ring and outer ring fault simulation experiments of the inter-shaft bearing were carried out. The simulation signal and the experimental signal were processed separately by the method established. The results show that the proposed method can enhance the fault signal and the ability of fault feature frequency extraction. After adaptively optimizing the structural parameters, the error between the extracted fault feature frequency and the theoreticalvalue of the fault frequency is less than 0.1%.