A Novel Multiple Feature-Based Engine Knock Detection System using Sparse Bayesian Extreme Learning Machine

Engine Knock Sensor Based on Symmetrical Rhomboid Structure-Encapsulated Fiber Bragg Grating

Due to the improvement of environmental protection emission standards, new energy vehicles fueled by natural gas and hydrogen green clean energy have developed rapidly. However, knock is one of the most important parameters that must be monitored for the safe operation of natural gas and hydrogen engines, so higher requirements are put forward for the reliability and durability of knock sensors. At present, the common knock sensors are mainly electronic sensors based on magnetostrictive and piezoelectric principles, and the sensing signals are easily interfered by electromagnetic interference during use, which is not conducive to the accurate measurement and control of knock. In this paper, a new resonant knock sensor based on fiber Bragg grating (FBG) is proposed to meet the actual needs of knock monitoring, and the FBG sensor unit is encapsulated with symmetrical rhomboid structure. The natural frequency of rhomboid structure is simulated and analyzed by Ansys software. The natural frequency of rhomboid structure is measured by applying transient impact. The resonance frequency of sensor is analyzed by Matlab software. The theoretical analysis is consistent with the measured value, which verifies the feasibility of the new knock sensor. Compared with the traditional engine knock sensor, this resonant engine knock sensor based on FBG has more advantages in anti-electromagnetic interference and multi-point networking, which provides a new method for knock monitoring of new energy engines.