Neural Networks as a Tool to Characterise Oil State After Porous Bearings Prolonged Tests

Król, Artur; Gocman, Krzysztof; Giemza, Bolesław

doi:10.5755/j01.ms.21.3.7506

Cited by 2 publications

(3 citation statements)

References 19 publications

(19 reference statements)

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“…ANN Accuracy [29] The research conducted in this study brings forth a novel approach to predicting the elemental spectroscopy of lubricants based on their electrical properties. The main objective of this study was to compare the performance of various soft computing models in predicting the elemental spectroscopy (Fe, Pb, Cu, Cr, Al, Si, and Zn) of lubricants based on their electrical properties (ε , ε , and tan δ).…”

Section: Knn Rbfmentioning

confidence: 99%

“…Lubricant condition monitoring is an area of active research, with soft computing techniques emerging as a promising focus. These techniques use artificial intelligence and machine learning to improve the accuracy and efficiency of lubricant maintenance, reducing the risk of downtime and costly repairs [15,[28][29][30][31]. Overall, soft computing techniques offer a faster [32], more accurate [33], and more adaptable [34] approach to diesel engine lubricant monitoring than traditional methods.…”

Section: Introductionmentioning

confidence: 99%

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Unlocking the Potential of Soft Computing for Predicting Lubricant Elemental Spectroscopy

Pourramezan,

Rohani,

Abbaspour-Fard

2023

Lubricants

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Predictive maintenance of mechanical systems relies on accurate condition monitoring of lubricants. This study assesses the performance of soft computing models in predicting the elemental spectroscopy (Fe, Pb, Cu, Cr, Al, Si, and Zn) of engine lubricants, based on the electrical properties (ε′, ε″, and tan δ) of oil samples. The study employed a dataset of 49 lubricant samples, comprising elemental spectroscopy and dielectric properties, to train and test several soft computing models (RBF, ANFIS, SVM, MLP, and GPR). Performance of the models was evaluated using error metrics such as MAPE, RMSE, and EF. The RBF model delivered the most accurate predictions for silicon at 7.4 GHz, with an RMSE of 0.4 and MAPE of 0.7. Performance was further improved by fine-tuning RBF parameters, such as the hidden size and training algorithm. The sensitivity analysis showed that utilizing all three input electrical properties (ε′, ε″, and tan δ) resulted in the lowest errors. Nevertheless, there are limitations to the study. In our country, measuring the electrical properties of engine lubricants and equipment is not a common practice, which leads to a limited number of samples studied. Despite these limitations, this study offers a proof-of-concept for predicting lubricant conditions based on readily measurable electrical properties. This paves the way for developing machine learning-based real-time lubricant monitoring systems.

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Section: Knn Rbfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unlocking the Potential of Soft Computing for Predicting Lubricant Elemental Spectroscopy

Pourramezan,

Rohani,

Abbaspour-Fard

2023

Lubricants

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“…Several research works on the issue of lubricant condition monitoring can be found in the literature, which are reviewed hereinafter, some of them have concentrated on modeling and using soft computing [11,[16][17][18][19]. Research was recently accomplished and applied a preliminary test on engine lubricant spectral analysis based on K-nearest neighbor (KNN) and Radial Basis Function (RBF).…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of the Models for Predicting the Rela-Tionship Between Electrical and Chemical Properties of Engine Lubricants: An Exact Monitoring Approach

Pourramezan¹,

Rohani²,

Abbaspour‐Fard³

2023

Preprint

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Condition monitoring is a primary principle for predictive maintenance. In this regard, several techniques have been employed, each of which has its strengths and weaknesses. In this work, the relationships between the electrical (ε', ε", tan δ) and chemical (Fe, Pb, Cu, Cr, Al, Si, Zn) properties of engine lubricants were investigated using soft computing models. The models' performance was evaluated considering some criteria, including RMSE and MAPE. The RBF model was determined as the best one to predict chemical properties (Fe, Pb, Cu, Cr, Al, Si, Zn) through electrical indexes (ε', ε", tan δ). The RBF’s parameters (e.g., hidden size and training algorithm) were then optimized for optimum performance. For instance, in the RBF model implemented to predict Al, the best hidden size and training algorithmic were 15 and 'trainlm', respectively. Finally, by sensitivity analysis it was observed that eliminating any of the inputs does not improve the performance of the RBF model performance. In general, the results of the current research showed that the electrical properties (e.g., ε', ε", tan δ) have the capability of estimating the contaminants in the lubricants which can improve the existing monitoring methods.

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Neural Networks as a Tool to Characterise Oil State After Porous Bearings Prolonged Tests

Cited by 2 publications

References 19 publications

Unlocking the Potential of Soft Computing for Predicting Lubricant Elemental Spectroscopy

Unlocking the Potential of Soft Computing for Predicting Lubricant Elemental Spectroscopy

Comparative Evaluation of the Models for Predicting the Rela-Tionship Between Electrical and Chemical Properties of Engine Lubricants: An Exact Monitoring Approach

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