Thus far, numerous studies have been conducted on fault diagnosis algorithms, e.g., expert systems, artificial neural networks (ANN), and fuzzy logic. With the improvement in the precision of the engine, automatic intelligent diagnosis technology with accurate recognition and prediction results is urgently required. Accordingly, FBG sensors are considered an ideal choice for detecting engine vibration.
FBG sensors exhibit compact structure, good insulation, easy installation, high reliability, easy to build sensor networks, and other prominent advantages. With the advancement of fiber optic sensing technology, a novel principle and method has been provided for condition monitoring and diagnosis of machinery. While engine is operating with different faults, the corresponding vibration signal will change, as reflected in the resonant frequency component changes. Vibration analysis is one of the techniques that have been used for engine condition monitoring and fault diagnosis successfully. Thus, to avoid fatal failure of valves or oil nozzle and to reduce the secondary damage caused by breakdowns, it is urgently required to detect valve or oil nozzle defects quickly and accurately. Any defect in valves or oil nozzle will lead to the loss of engine power and even cause permanent damage. The working state of valve or oil nozzle is an important condition for engines, covering all petrol engines and diesel engines. Furthermore, the proposed method can achieve higher performance than other methods in the fault identification accuracy. The results here reveal that the proposed method exhibits excellent fault detection performance for ICE fault identification. Experimental tests on the valve fault and fuel injection advance angle fault were performed and presented to verify the efficacy of the proposed approach. Subsequently, a fault identification model was built based on multiclass υ-support vector classification ( υ-SVC). Moreover, vibration energy was taken as fault characteristics. Structural vibration signal containing fault information of engine valves and oil nozzle was identified by FBG sensors and preprocessed using wavelet decomposition and reconstruction. In the present study, a high-frequency vibration system was proposed based on Fiber Bragg Grating (FBG) cantilever sensor and intelligent algorithm. State monitoring and fault diagnosis of an internal combustion engine are critical for complex machinery safety.