Prediction of RUL of Lubricating Oil Based on Information Entropy and SVM

Liu, Zhongxin; Wang, Huaiguang; Hao, Mingxing; Wu, Dinghai

doi:10.3390/lubricants11030121

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…Some issues, related to skewed data distribution, were overcome here by removing outliers and logarithmic transformation. Undoubtedly, this dataset and similar oil analysis results can be processed with more advanced statistical or machine learning models such as quantile regression, decision trees [ 63 , 64 ], SVM [ 65 , 66 ], artificial neural networks [ 67 ], or their assemblies [ 68 , 69 ]. There are enough training data, and the computational cost is moderate, but the main challenge is to properly formulate the prediction problem when engine wear and its remaining useful life is not a priori known.…”

Section: Discussionmentioning

confidence: 99%

Correlative Method for Diagnosing Gas-Turbine Tribological Systems

Deliś

Kłysz

Przysowa

2023

Sensors

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Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry and analyzed with a correlative model. Customized alarm limits were determined for iron by binning aluminum and zinc concentration into four levels. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests was carried out to study the impact of aluminum and zinc concentration on iron concentration. A strong correlation between iron and aluminum, as well as a weaker but still statistically significant correlation between iron and zinc, was observed. When the model was applied to evaluate a selected engine, deviations of iron concentration from the established limits indicated accelerated wear long before the occurrence of critical damage. Thanks to ANOVA, the assessment of engine health was based on a statistically proven correlation between the values of the dependent variable and the classifying factors.

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Section: Discussionmentioning

confidence: 99%

Correlative Method for Diagnosing Gas-Turbine Tribological Systems

Deliś

Kłysz

Przysowa

2023

Sensors

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“…Lubricating oil is a widely used lubricant in industry, whose primary function is to reduce friction and wear of moving parts [1,2]. In addition, lubricating oil can also prevent metal corrosion, excellent mechanical components, and clean friction surfaces, extending the machinery's service life and ensuring the equipment's regular operation [3,4]. With the use of the machine, the lubricating oil gradually deteriorates until it cannot meet the working needs of the machine.…”

Section: Introductionmentioning

confidence: 99%

“…Based on environmental protection and waste reduction considerations, maximizing the use time of lubricating oil seems to be the best method. However, untimely replacement of lubricating oil can exacerbate mechanical wear, increase the risk of equipment damage, and even cause serious accidents [4]. According to statistics, about 30% of the world's primary energy is wasted due to friction, over 80% of machine components fail due to excessive wear, and more than half of mechanical equipment malignant accidents stem from lubrication failure [7,8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of remaining useful life of lubricating oil based on optimal BP neural network

Liu,

Wang,

et al. 2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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Choosing an appropriate lubricating oil replacement strategy is crucial for the machine's operation and maintenance. Based on the concept of condition-based maintenance (CBM), this article proposes a method for predicting the remaining useful life (RUL) of lubricating oil using lubrication condition monitoring (LCM) data and machine learning (ML) theory. Firstly, obtain lubricating oil samples through engine bench tests and quantitatively analyze the elemental content of the lubricating oil in use using atomic emission spectroscopy (AES). Then, a method for finding the optimal back propagation (BP) neural network was proposed to construct a lubricating oil RUL prediction model. The content of 12 elements in lubricating oil is used as the input variable, and the three states of lubricating oil are used as the output variable. Finally, by comparing with the lubricating oil RUL prediction model based on support vector machine (SVM), it is shown that the proposed optimal BP neural network model has better accuracy and robustness.

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“…Effectiveness of multilayer perceptron and radial basis function neural networks in RUL estimation of ball bearings has been investigated [3]. A support vector machine combining with information entropy preprocessing was proposed to predict RUL of lubricating oil [4]. A gradient boosting decision tree model in conjunction with relative entropy distance-based fault severity was integrated to estimate RUL of electronic elements [5].…”

Section: Introduction 11 Related Workmentioning

confidence: 99%

Remaining Useful Life Estimation based on Noise Injection and a Kalman Filter Ensemble of modified Bagging Predictors

2023

KSII TIIS

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Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

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Prediction of RUL of Lubricating Oil Based on Information Entropy and SVM

Cited by 7 publications

References 31 publications

Correlative Method for Diagnosing Gas-Turbine Tribological Systems

Correlative Method for Diagnosing Gas-Turbine Tribological Systems

Prediction of remaining useful life of lubricating oil based on optimal BP neural network

Remaining Useful Life Estimation based on Noise Injection and a Kalman Filter Ensemble of modified Bagging Predictors

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