Research On Fault Diagnosis System Of 8V396 Diesel Engine

Diesel engines are widely used in various fields such as long-distance transportation,generator sets,engineering machinery,and renewable energy conversion due to their characteristics of high output torque,high combustion efficiency,and good fuel economy.However,the diesel engine system is large and complex,which increases the probability of faults,and the diesel engines work in complex and diverse environments with high salt,high cold,and high dust.Moreover,Inappropriate use,maintenance,and care may exist.Therefore,various failures are inevitable for diesel engines.Once a failure occurs,it will affect normal production operations,cause huge economic losses,and even threaten the safety of personnel.Therefore,it is necessary to monitor the state parameters of diesel engines,analyze their operating data characteristics,diagnose and predict potential failures to ensure the safe and reliable operation of diesel engines.This paper focuses on the development of a diesel engine fault diagnosis system,with the 8V396 diesel engine as the research subject.The system comprises a data acquisition unit and a monitoring and analysis human-machine interface(HMI).The data acquisition unit collects the state parameters of the diesel engine,performs tasks such as preprocessing and data transmission,while the monitoring and analysis HMI facilitates human-machine interaction,displays real-time monitoring results,and enables functions such as fault alarms,fault analysis,and decision support.In terms of diagnostic algorithm research,a diesel engine cylinder misfire fault diagnosis method based on the instantaneous speed signal is proposed.Initially,the instantaneous speed signal is acquired to preprocessing,including outlier removal,filtering,and normalization.The methods employed for these steps are the median threshold method,wavelet threshold denoising method,and maximum-minimum normalization method,respectively.Signal analysis is conducted to extract 16 types of timefrequency domain and dimensionless features from the instantaneous speed signal,with the aim of selecting an optimal feature set.The particle swarm optimization algorithm is utilized to optimize the preselected features.Finally,a classification model is established based on the support vector machine(SVM)method,and the model’s performance is evaluated using tenfold cross-validation.Verification experiments are conducted on the 8V396 diesel engine test bench under normal operating conditions,lubricating oil leakage scenarios,and singlecylinder misfire conditions to validate the fault diagnosis system and the diesel engine cylinder misfire fault diagnosis method.The experimental results demonstrate that the 8V396 diesel engine fault diagnosis system can monitor the temperature,pressure,and other parameters at various measurement points of the diesel engine group in real time,analyze the characteristics of the instantaneous speed,diagnose faults,issue alarms when malfunctions occur,assist crew members in understanding the operational status of the diesel engine group,provide fault warnings,and offer operational and maintenance recommendations.