Bearing Fault Diagnosis based on Stochastic Resonance with Cuckoo Search

Abstract

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.

Effect of scale-varying fractional-order stochastic resonance by simulation and its application in bearing diagnosis

“…Some algorithms have been used in SR such as particle swarm optimization, 29 genetic algorithms, 30 ant colony algorithm, 15 and cuckoo search algorithm. 18 Cuckoo search algorithm is used in our proposed SFrSR method for seeking the optimal parameters of SR. Improving the performance of the heuristic algorithms is a research direction of the adaptive SR. (iv) Appropriate search ranges of the parameter (H, K, α) enable the optimization algorithm to find the optimal parameters quickly and accurately.…”

“…Most studies used SNR or its improvement as the criterion. 11,13,14,18,29,30 However, they are unavailable when the target frequency is unknown. As to this problem, some criterions are proposed such as approximate entropy, 12 MPSK index, 17 mutual information, 31 and weighted power spectrum kurtosis.…”

An adaptive stochastic resonance method based on multi-agent cuckoo search algorithm for bearing fault detection

“…A criterion is needed to evaluate the SR effect. Some indexes have been proposed such as approximate entropy [11], signal-to-noise ratio (SNR) [16], local signal to noise ratio (LSNR) [13] and weighted power spectrum kurtosis (WPSK) [17]. Because of the precise definition and easy application, LSNR will be used as the evaluation criterion:…”

“…Cuckoo search (CS) algorithm is a new heuristic optimization algorithm and has been used in many fields [13,[18][19][20]. It simulates the parasitic reproductive strategy of cuckoos: the solutions are seen as cuckoo eggs, the optimal solution which has the optimal fitness as the optimal cuckoo egg, the feasible region as the search zone of the cuckoos.…”

“…If the barrier is too low, the effect of SR is not obvious. Heuristic optimization algorithms can search the optimal parameters of SR easily and have been applied in adaptive SR. For example, Chi et al [13] used an adaptive SR based on cuckoo search algorithm for bearing fault diagnosis. López et al [14] applied the underdamped SR based on particle swarm in weak signal detection.…”