A novel memristor chaotic circuit and its application in weak signal detection of wind turbine fault
Abstract
With the rapid development of wind power generation in recent years, the demand for detecting weak signals of wind turbine faults has become more urgent. This paper introduces a novel memristor chaotic circuit constructed based on third-order magnetically memristors. The Melnikov chaotic condition of this circuit is analyzed, and its dynamical characteristics are studied through phase trajectory diagrams, bifurcation diagrams, Lyapunov exponent spectra, and Poincaré maps. Leveraging the initial value sensitivity and noise immunity of chaotic systems, the memristor chaotic circuit is employed for the detection of weak signals in wind turbine faults. Using the chaotic system state transition method, we find the threshold for the circuit state to transition from chaotic state to large-scale periodic state, adjust the parameters to make the system in a critical state, input the wind turbine fault vibration signal, and detect the fault signal based on its state transition. Subsequently, the chaotic resonance method is employed, introducing the signal under test, which contains high-intensity chaotic noise, into this novel memristive circuit. This results in chaotic resonance, causing the noise components to be concentrated toward the frequency region where the weak signal under test is located, thereby enhancing the fault signal and facilitating fault identification. The results indicate that this novel memristor chaotic circuit possesses advantages such as high accuracy, strong noise immunity, straightforward operation, and clear judgment in the field of weak signal detection. This circuit shows promising applications in the field of weak signal detection.
