Online Monitoring of Automotive Engine Lubricating Oil Based on Internet of Things Technology

Abstract

In order to realize the online monitoring of automotive engine lubricating oil, a method based on Internet of Things technology is proposed. This method uses the self-organizing neural network of the Internet of Things to fuse the original multidimensional feature data to obtain the fusion value. The Parzen window method is used to formulate the limit value of fusion value, and the samples are divided into three states: normal, warning, and abnormal. Weka software is used to extract rules from oil data. This method can identify different wear state information from oil spectral data, extract knowledge rules, and use them to build the knowledge base of automobile engine wear diagnosis system, so as to realize the automation and intelligence of automobile engine fault diagnosis based on lubricating oil spectral wear data. The measurement method of the lubricating oil sensor is mainly to comprehensively reflect the relationship between oil quality and electrical signal, so as to effectively provide users with reliable information. After many studies, it is concluded that the conductivity of lubricating oil has a good linear relationship with its acid value, metal particles, moisture content, and the change of additive content. Measuring the change of conductivity is an effective means to detect the change of lubricating oil quality. The experimental results show that using the extracted knowledge rules to verify the state of samples, the recognition rate is 97.47%. In order to more fully explain the difference between important element fusion and all feature fusion, all features are extracted. At the same time, the fault diagnosis and recognition rate of all features is not high, only 62.39%. It is proved that the Internet of Things technology can effectively realize online monitoring of the automotive engine lubricating oil.