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

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.

[1]  Jee-Hyong Lee,et al.  An approach for multi-label classification by directed acyclic graph with label correlation maximization , 2016, Inf. Sci..

[2]  Lixiang Duan,et al.  A new feature extraction approach using improved symbolic aggregate approximation for machinery intelligent diagnosis , 2019, Measurement.

[3]  Jussi Salo,et al.  Calculated estimate of FBG sensor’s suitability for beam vibration and strain measuring , 2014 .

[4]  Francis Berghmans,et al.  Vibration Monitoring Using Fiber Optic Sensors in a Lead-Bismuth Eutectic Cooled Nuclear Fuel Assembly † , 2016, Sensors.

[5]  Mangesh B. Chaudhari,et al.  Detection of Combined Gear-Bearing Fault in Single Stage Spur Gear Box Using Artificial Neural Network , 2016 .

[6]  V. Sugumaran,et al.  Fault diagnosis of monoblock centrifugal pump using SVM , 2014 .

[7]  Giorgio Sulligoi,et al.  A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks , 2016 .

[8]  Qingsong Ai,et al.  Intelligent monitoring and diagnosis for modern mechanical equipment based on the integration of embedded technology and FBGS technology , 2011 .

[9]  Tahar Bahi,et al.  Condition Monitoring and Fault Detection in Wind Turbine Based on DFIG by the Fuzzy Logic , 2015 .

[10]  Ming J. Zuo,et al.  Vibration signal models for fault diagnosis of planet bearings , 2016 .

[11]  Bin Gu,et al.  A Robust Regularization Path Algorithm for $\nu $ -Support Vector Classification , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Yanchun Liang,et al.  Multi-BP expert system for fault diagnosis of powersystem , 2013, Eng. Appl. Artif. Intell..

[13]  Stephen M. Schultz,et al.  Instrumentation of integrally stiffened composite panel with fiber Bragg grating sensors for vibration measurements , 2015 .

[14]  Meng Joo Er,et al.  Sequential fuzzy clustering based dynamic fuzzy neural network for fault diagnosis and prognosis , 2016, Neurocomputing.

[15]  Zhenyuan Zhong,et al.  Fault diagnosis for diesel valve trains based on time–frequency images , 2008 .

[16]  Yonghong Liu,et al.  Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge , 2015 .

[17]  Steve Vanlanduit,et al.  Development of an Optical Fiber Sensor Interrogation System for Vibration Analysis , 2016, J. Sensors.

[18]  Wang Bin,et al.  Expert System of Fault Diagnosis for Gear Box in Wind Turbine , 2012 .

[19]  Belkacem Ould-Bouamama,et al.  A novel gearbox fault feature extraction and classification using Hilbert empirical wavelet transform, singular value decomposition, and SOM neural network , 2018 .

[20]  Andrew D. Ball,et al.  An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks , 2014, Expert Syst. Appl..

[21]  Zhiwen Liu,et al.  Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings , 2013, Neurocomputing.

[22]  H. Abdul Razak,et al.  Fault diagnosis on beam-like structures from modal parameters using artificial neural networks , 2015 .

[23]  Junsheng Cheng,et al.  A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination , 2014 .

[24]  Baoping Tang,et al.  A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm , 2013 .