Internal Combustion Engine Fault Identification Based on FBG Vibration Sensor and Support Vector Machines Algorithm

Abstract EN

State monitoring and fault diagnosis of an internal combustion engine are critical for complex machinery safety.

In the present study, a high-frequency vibration system was proposed based on Fiber Bragg Grating (FBG) cantilever sensor and intelligent algorithm.

Structural vibration signal containing fault information of engine valves and oil nozzle was identified by FBG sensors and preprocessed using wavelet decomposition and reconstruction.

Moreover, vibration energy was taken as fault characteristics.

Subsequently, a fault identification model was built based on multiclass υ-support vector classification (υ-SVC).

Experimental tests on the valve fault and fuel injection advance angle fault were performed and presented to verify the efficacy of the proposed approach.

The results here reveal that the proposed method exhibits excellent fault detection performance for ICE fault identification.

Furthermore, the proposed method can achieve higher performance than other methods in the fault identification accuracy.