Rolling bearings are the main components of modern machinery, and harsh operating environments often make them prone to failure. Therefore, detecting the incipient fault as soon as possible is useful for bearing prognostics and health management. However, the useful feature information relevant to the bearing fault contained in the vibration signals is weak under the influence of the noise and transmission path. The useful feature information is even submerged in the noise. Thus, it becomes difficult to identify the fault symptom of rolling bearings in time from the vibration signals. Stochastic resonance (SR) is a reliable method to detect the weak signal in intense noise. However, the effect of SR depends on the adjustment of two parameters. Cuckoo Search (CS) is a heuristic novel optimization algorithm that can search the global solution quickly and efficiently. Thus, CS is utilized to optimize the two parameters in this paper. Local signalto-noise ratio (LSNR) is used to evaluate resonance effect. Two bearing fault datasets were used to confirm the effectiveness of SR optimized by CS. SR methods optimized by particle swarm optimization (PSO), genetic algorithms (GA), firefly algorithm (FA), and ant colony optimization (ACO) are also used to detect the bearing fault signal in the two datasets. The analysis results state SR optimized by CS can find better LSNR than SR optimized by other algorithms no matter if it is in the same iterations or in the same computation time, thereby making the fault feature more obvious.
Bearing and planetary gearbox are important for rotating machinery. However, their faults often cause the stop of the machinery or even fatal casualties. Vibration signal contains the status information of the rotating machinery, which is covered by the strong noise. Stochastic resonance (SR) is a noise-benefit phenomenon, which can detect the weak fault characteristic signal from the vibration signal under strong noise. To detect the fault of bearing or planetary gearbox effectively, SR based on Wood-Saxon potential which only has on potential well called WSSR is studied, and a novel fault diagnosis strategy based on WSSR is proposed. The effect of every WSSR parameter, anti-noise capability of WSSR under different noise intensities and optimal frequency response of WSSR under different driving frequency are analyzed by simulation. To verify the effectiveness of our proposed fault diagnosis strategy based on WSSR, three preset fault tests of bearing and two of planetary gearbox are carried out. Bi-stable SR is also used for comparison. The results show that our proposed fault diagnosis strategy is more effective for the fault detection of bearing and planetary gearbox than bi-stable SR.
Stochastic resonance is like a nonlinear filter to detect the weak bearing fault-induced impulses that submerged in strong noises. Signal-to-noise ratio (SNR) is often used as the index to evaluate the SR output, but the fault characteristic frequency (FCF) must be known in order to calculate SNR. A novel bearing fault diagnosis method called synthetic quantitative index-based adaptive underdamped stochastic resonance (SQI-AUSR) is proposed. The synthetic quantitative index (SQI) is composed of power spectrum kurtosis, kurtosis, margin index, and correlation coefficient. The SQI is independent of FCF, which avoids the limitation that the calculation of SNR must know the FCF. Numeric simulations and two case studies of bearing faults are carried out. The results show that (1) the SQI is more effective than other proposed indexes such as correlation coefficient and weight power spectrum kurtosis and (2) the proposed SQI-AUSR is effective for bearing fault diagnosis and is better than SNR-AOSR.
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