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Applying Capacitance/Inductance Measurements for Characterizing Oil Debris and pH


  • Young Researchers and Elite Club, Hamedan Branch, Islamicazad University, Hamedan, Iran, Islamic Republic of


Lubricating oil is important in internal-combustion engines. The present study investigates about constructing capacitor for measurement of pH and debris in oil. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) for predicting oil debris and pH are used. For measurement of pH and oil debris, two capacitor were constructed using two aluminum and two copper plate electrodes have a distance of 20 mm, with each other respectively. The best ANN model for predicting pH and oil debris was found as two hidden layers network with 1-5-1-1 structure. RMSE-test, RMSE-train and time for the best ANN model were reported as 0.17, 0.21 and 853 ms, and 0.18, 0.18, 853 ms respectively. The corresponding RMSE-test, RMSE-train and time values for the best ANFIS topology for predicting pH and oil debries were 0.16, 0.68, 17 and 6.7, 22.4, 23 respectively. It is concluded that ANN with lower RMSE-test was better than ANFIS for predicting.


Aluminum Plate Electrodes, Artificial Neural Networks, Adaptive Neuro Fuzzy Inference System, Copper Plate Electrodes, Lubricating.

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  • Agoston A, Otsch C, Jakoby B. Viscosity sensors for engine oil condition monitoring- Application and interpretation of results. Sensors and Actuators A: Physical. 2005; 121:327–32.
  • Appleby M. Oil debris and viscosity monitoring using ultrasonic and capacitance measurements. University of Akron August; 2010.
  • Appleby M, Choy FK, Du L, Zhe J. Oil debris and viscosity monitoring using ultrasonic and capacitance/inductance measurements. Lubrication Science. 2013; 25:507–24.
  • Borin A, Poppi RJ. Application of mid infrared spectroscopy and iPLS for the quantification of contanminants in lubricating oil. Vib Spectrosc. 2005; 37:27–32.
  • Han T, Yang BS, Yin ZJ. Feature-based fault diagnosis system of induction motors using vibration signal. Journal of Quality in Maintenance Engineering. 2007; 13:163–75.
  • Herzog MA, Marwala T, Heyns PS. Machine and component residual life estimation through the application of neural networks. Reliability Engineering and System Safety. 2009; 94:479–89.
  • Jang JSR. ANFIS- Adaptive Network-based-Fuzzy Inference System. IEEE Trans on Systems, Man and Cybernetics. 1993; 23:665–85.
  • Ko YG, Kim CH. Confirmation of heavy metal ions in used lubricating oil from a passenger car using chelating self-assembled monolayer. Journal of Colloid and Interface Science. 2006; 301:27–31.


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