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Evaluation of lubricant condition and engine health based on soft computing methods

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Abstract

Evaluating the condition of the lubricant plays an influential role in the maintenance engineering of mechanical systems. This study has several objectives and finally offers a method based on the neural network. That method uses more limited indicators to determine the condition of the lubricant and the engine's health. That will reduce testing costs and encourage equipment and machine owners to perform lubricant analyses. The 681 results of the engine lubricant spectral analysis had used. The statistical analysis results showed; out of twelve indexes, only seven indexes, including iron, chromium, lead, copper, aluminum, nickel, and TDPQ, could have been influential in determining the three conditions of normal, caution, and critical wear. Soft computing methods, including KNN and RBF-ANN, were used to diagnose engine health conditions in three classes of normal, caution, and critical based on seven indexes of engine lubricant in three sizes of the training data set includes 40, 60, and 80%. The results showed that the engine health diagnosis accuracy by KNN of the training set sizes of 80, 60, and 40% was equal to 99.71, 98.38, and 97.36%, while detection accuracy of the RBF-ANN for all three training set sizes was approximately 99.85%. Also, the sensitivity analysis results showed soft computing methods could have the high ability to diagnose engine health.

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Abbreviations

ANN:

Artificial neural network

FN:

False condition negative

FP:

False condition positive

KNN:

K-nearest neighbor

GA:

Genetic algorithm

MLP:

Multilayer perceptron

PQ:

Particle quantifier

RBF:

Radial basic function

RFE:

Recursive feature elimination

TAN:

Total acid number

TDPQ:

Time depending on the particle quantifier

TN:

True condition negative

TP:

True condition positive

TSSE:

Total sum squared error

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Acknowledgements

We thank the Ferdowsi University of Mashhad in Iran for funding the research project and Tirage Company for making its maintenance database available.

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Authors and Affiliations

  1. Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

    Mohammad-Reza Pourramezan & Abbas Rohani

  2. Department of Biosystems Engineering, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

    Nemat Keramat Siavash & Mohammad Zarein

Authors
  1. Mohammad-Reza Pourramezan
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  2. Abbas Rohani
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  3. Nemat Keramat Siavash
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  4. Mohammad Zarein
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Correspondence to Abbas Rohani.

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Pourramezan, MR., Rohani, A., Keramat Siavash, N. et al. Evaluation of lubricant condition and engine health based on soft computing methods. Neural Comput & Applic 34, 5465–5477 (2022). https://doi.org/10.1007/s00521-021-06688-y

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  • DOI: https://doi.org/10.1007/s00521-021-06688-y

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