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.