LAUSR

The principle of stochastic resonance (SR) has been intensively used in designing algorithms for incipient bearing fault diagnosis based on stochastic simulation. In order to directly apply the semi-analytic result to the laboratory designs, an incipient bearing fault diagnosis algorithm based on the method of moments for SR systems was proposed, with its real-time monitoring efficiency being assured by an available table of the involving stationary moments. Nevertheless, since the noise that buried the fault signal can only be estimated after sampling, the calculation of such stationary moments might be an inconvenience. To fix this inconvenience, a brand-new fault diagnosis algorithm is designed in this article based on the two-state theory for a biased underdamped bistable SR system. The capability and the efficiency of the algorithm in identifying the characteristic frequency of the hidden fault signal are confirmed with the experimental datasets. This investigation actually opens a new way for improving the existing SR-based diagnosis algorithms.