Application of Self-adaptive Cuckoo Search Algorithm for Bearing Fault Diagnosis

Background: Bearing is a key component of rotating machinery, and its operating condition directly affects the performance of the whole machine. Methods: Based on the investigation of related papers and patents, a fault diagnosis model using the self-adaptive cuckoo search algorithm combined with BP neural network (SaCSBP) is proposed for the effective identification of bearing fault location and loss degree. With respect to the Selfadaptive Cuckoo Search (SaCS) algorithm, a dimension by dimension improvement strategy is introduced to enhance the local search capability, and the control parameters are then set according to the solution quality. Results: The proposed SaCS is compared with several other algorithms on 14 benchmark functions, and the experimental results demonstrate that SaCS exhibits a better or comparable performance. Moreover, SaCSBP obtains the highest fault recognition accuracy. Conclusion: The proposed method has strong fault tolerance and can accurately identify different types and severities of bearing faults.