Mafla-Yépez Carlos, Castejon Cristina, Rubio Higinio, Morales Cesar
Grupo de Investigación de Ciencias en Red eCIER, Universidad Técnica del Norte, Ibarra 100105, Ecuador.
MAQLAB Research Group, Dpto, Ing. Mecánica, Pedro Juan de Lastanosa Reseach Institute, Universidad Carlos III de Madrid, 28911 Leganes, Spain.
Sensors (Basel). 2024 Feb 28;24(5):1551. doi: 10.3390/s24051551.
This research focuses on the analysis of vibration of a compression ignition engine (CIE), specifically examining potential failures in the Fuel Rail Pressure (FRP) and Mass Air Flow (MAF) sensors, which are critical to combustion control. In line with current trends in mechanical system condition monitoring, we are incorporating information from these sensors to monitor engine health. This research proposes a method to validate the correct functioning of these sensors by analysing vibration signals from the engine. The effectiveness of the proposal is confirmed using real data from a Common Rail Direct Injection (CRDi) engine. Simulations using a GT 508 pressure simulator mimic FRP sensor failures and an adjustable potentiometer manipulates the MAF sensor signal. Vibration data from the engine are processed in MATLAB using frequency domain techniques to investigate the vibration response. The results show that the proposal provides a basis for an efficient predictive maintenance strategy for the MEC engine. The early detection of FRP and MAF sensor problems through a vibration analysis improves engine performance and reliability, minimizing downtime and repair costs. This research contributes to the advancement of monitoring and diagnostic techniques in mechanical engines, thereby improving their efficiency and durability.
本研究聚焦于压燃式发动机（CIE）振动分析，特别考察对燃烧控制至关重要的燃油轨压力（FRP）传感器和空气质量流量（MAF）传感器中的潜在故障。顺应机械系统状态监测的当前趋势，我们正纳入来自这些传感器的信息以监测发动机健康状况。本研究提出一种通过分析发动机振动信号来验证这些传感器正确功能的方法。使用来自共轨直喷（CRDi）发动机的实际数据证实了该提议的有效性。使用GT 508压力模拟器进行的模拟模拟了FRP传感器故障，并用一个可调电位器操控MAF传感器信号。利用频域技术在MATLAB中处理发动机的振动数据，以研究振动响应。结果表明，该提议为MEC发动机的高效预测性维护策略提供了基础。通过振动分析早期检测FRP和MAF传感器问题可提高发动机性能和可靠性，将停机时间和维修成本降至最低。本研究有助于推进机械发动机的监测和诊断技术，从而提高其效率和耐用性。
