Analysis And Research On Cold Test Of Diesel Engine Based On Big Data Analysis

With the development of the automobile manufacturing industry,the sales volume of automobiles has been maintained at a relatively high level,and the requirements for the internal combustion engine as the core component of the automobile are getting higher and higher.Therefore,the inspection of the assembly quality of the internal combustion engine is particularly important.Traditional hot test has many drawbacks.In contrast,the cold test technology developed in recent years has gradually become the most important method for the assembly quality inspection of internal combustion engines of major manufacturers due to its many advantages such as high degree of automation,low pollution,accurate fault location,and low cost.The research on cold test technology has gradually become a popular direction.The research on cold test technology in this article is based on big data analysis.The main problems dealt with include:determining the control limits of different diesel engine quality inspections for different data distribution patterns;Carry out noise reduction processing on the diesel engine cold test vibration signal,and carry out time-frequency analysis on it;classify the diesel engine assembly parameters to determine the quality of the assembly.The article first introduces the diesel engine cold test bench system,analyzes some cold test items,uses the collected cold test data,and uses big data analysis to determine the distribution of the data.For normal distribution,through Kolmogorov-Smirnov’s square and Q-Q graph method verify it.For non-normal distributions such as positive skew distribution and negative skew distribution,the concept of Z-score is introduced,and non-normal data is converted into normal by using different formulas.The transformed normal distribution adopts the 3σ principle to determine the control limits of the parameters,forming a relatively complete set of cold test control limit processing procedures.Aiming at the problem of noise in the cold test vibration signal,the wavelet threshold denoising method is improved.The function for obtaining the threshold is improved,and the method of combining soft and hard thresholds is used to process the highfrequency coefficients of different layers to form a new Signal noise reduction method.This method is used to process the analog noise signal,compared with several commonly used noise reduction methods to verify the processing effect of this method.Then,on this basis,time-frequency analysis was performed on the vibration signal,the low-speed and high-speed cylinder head vibration signals during the cold test were selected,and first,the signal is processed by an improved noise reduction method,and then two time-frequency transformations are developed to summarize the timefrequency characteristics between the amplitude and frequency of the low-speed and high-speed cylinder head vibration signal and the crankshaft angle.Aiming at the problem of diesel engine assembly failure and quality difference,the SVM is used to classify the cold test vibration on the basis of experimental data.The high-speed cylinder head vibration parameters are selected as the research object.For the two analyses,fault and normal limits are set,and feature vectors of different factors are constructed respectively.Perform classification labeling and normalization processing on the feature vector.Then set various parameters and build the model on the basis of various optimization,use Matlab software to program to train the test model,and analyze its quality,and verify the construction through multiple classification and recognition The classification accuracy of the model.