Estimation of soot and fuel invasion in diesel engine oils through a combination of dielectric constant sensor and viscosity sensor

To satisfy the latest emission standards, the use of advanced technologies such as exhaust gas recirculation, diesel particulate filter, and complicated injection strategies are increasing in modern diesel engines. However, some of these complicated technologies may cause soot and diesel fuel to enter the engine oil during engine operation and ultimately affect oil performance. Once the soot and diesel fuel content is beyond a certain level, the engine oil should be changed to guarantee adequate lubrication. Thus, a proper method of monitoring oil condition is required. It is well known that soot and diesel fuel affect oil permittivity and viscosity significantly. Thus, in this study, a new method to monitor oil quality is proposed by measuring the dielectric constant and oil viscosity. Carbon black was used as the substitute for soot and was mixed with diesel fuel at different ratios. Both the dielectric constant and oil viscosity increase as soot content increases. Diesel fuel content affects the viscosity and slightly affects the dielectric constant. Multivariable linear regression and an artificial neural network were used to correlate the dielectric constant and viscosity with the oil conditions, and a prediction model was established. The predicted results indicate good agreement with the experimental results. It is believed that with the developed algorithm, this method could potentially be used for the online estimation of engine oil soot and oil dilution conditions.