The weak-signal detection technologies based on stochastic resonance (SR) play important roles in the vibration-based health monitoring and fault diagnosis of rolling bearings, especially at their early-fault stage. Aiming at the parameter-fixed vibration signals in practical engineering, it is feasible to diagnose the potential rolling bearing faults through adaptively adjusting the SR system parameters, as well as other generalized parameters such as the amplitude-transformation coefficient and scale-transformation coefficient. However, extant adaptive adjustment methods focus on the system parameters, while the adjustments of other adjustable parameters have not been fully studied, thus limiting the detection performance of the adaptive SR method. In order to further enhance the detection performance of adaptive SR methods and extend their application in rolling bearing fault diagnosis, an adaptive multiparameter-adjusting SR (AMPASR) method for bistable systems based on particle swarm optimization (PSO) algorithm is proposed in this paper. This method can produce optimal SR output through adaptively adjusting multiparameters, thus realizing fault feature extraction and further fault diagnosis. Furthermore, the influence of algorithm parameters on the optimization results is discussed, and the optimization results of the Langevin system and the Duffing system are compared. Finally, we propose a weak-signal detection method based on the AMPASR of the Duffing system and employ three diagnosis examples involving inner ring fault, outer ring fault, and rolling element fault diagnoses to demonstrate its feasibility in rolling bearing fault diagnosis.

No abstract

No abstract

In the current spectral simulation method based on a digital micromirror device (DMD)， the spectral simulation units have different bias properties and nonlinear modulation； thus， a spectral simula• tion method for multi-color temperature modulation is lacking. This paper presents a fuzzy proportional-in• tegral-derivative (PID) control-based stellar spectral simulation method. First， the DMD working ma• trix， spectral modulation weight matrix， spectral distribution function matrix， and target spectrum matrix are constructed. Next， a spectral distribution function fitting algorithm based on a genetic algorithm-opti• mized backpropagation (BP) neural network is studied. The BP neural network algorithm and the basic el• ements of the genetic algorithm are designed and used to achieve spectral distribution function fitting in the peak wavelength region of 400-800 nm. Then， a spectral simulation algorithm based on fuzzy PID control is proposed. The fuzzy set and affiliation function are selected， and the fuzzy inference and defuzzification rules are formulated. The fuzzy PID controller is simulated and analyzed， and the results indicate that the overshoot of fuzzy PID control is reduced by 90. 7% and the regulation time is shortened by 69. 4% com • 文章编号 1004-924X (2023)11-1619-12 收稿日期： 2022-12-07； 修订日期： 2023-01-05. 基金项目： 吉林省科技发展计划资助项目(No. 20200401046GX) 第 31 卷 光学 精密工程pared with those of PID control. Finally， the simulation accuracy of the color temperature spectral distribu• tion curve in the range of 3 000-11 000 K is verified via experiments. According to the results， the spectral simulation error is better than ±4. 21%. Compared with the PID control， the maximum spectral simula• tion accuracy of fuzzy PID control at 3 000， 6 500， and 11 000 K is increased by factors of 2. 31， 1. 71， and 2. 02， respectively. The proposed method can increase the spectral simulation accuracy， providing the theory and foundation for the development of high-precision star-sensitive devices.