With the popularization of electronic control systems in engines, traditional fault diagnosis methods are no longer able to meet the demand. This article combines case analysis to explore the application of intelligent diagnostic technology.

1. Analysis of typical fault cases

Idle shaking: A certain German sedan experienced unstable idle after a cold start, and the fault code displayed an abnormal signal from the throttle position sensor. Through oscilloscope testing, it was found that the voltage fluctuation range of the sensor signal exceeded ± 5%. After replacing the sensor, the fault was resolved.

Cooling system failure: A certain SUV had a water temperature alarm while driving at high speed, and upon inspection, it was found that the thermostat valve was stuck. Using an infrared thermometer to compare the readings of the engine cylinder block and water temperature gauge, with an error of 25 ℃, it was confirmed that the sensor was faulty.

2. Intelligent diagnostic technology

Neural network model: A fault prediction system is constructed based on BP neural network, with input parameters including 12 features such as vibration frequency and oil pressure. Testing was conducted on a certain engine, and the accuracy of fault identification reached 92%.

Augmented Reality (AR) Assisted Maintenance: By overlaying virtual maintenance guidance with AR glasses, technician troubleshooting time is reduced by 40%. For example, when replacing the high-pressure oil pump, the AR system can display torque parameters and installation sequence in real time.