J69-T-25A engine component failure analysis

Manarvi, Irfan Anjum; Qazi, Muhammad Asim

doi:10.1109/aero.2014.6836389

Cited by 2 publications

(2 citation statements)

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“…Analyzing component failures based on their frequency aids in the identification of failing parts. 29 Correlations between parameters such as boron content, acid number, base number, and oil service life can be unveiled through the analysis of used engine oils throughout their service life. 22,30 The adjustment of engine oil replacement frequency may be necessary based on operating conditions, such as urban settings, and other factors accelerating oil aging and part wear.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the evident advantages of engine oil analysis, background research highlights a lack of definitive answers regarding the frequency at which it should be performed on diesel engines. ,− The frequency of engine oil analysis may necessitate adjustment as diesel engines age, requiring increased effort for reliability maintenance despite improving experience with aging. Analyzing component failures based on their frequency aids in the identification of failing parts . Correlations between parameters such as boron content, acid number, base number, and oil service life can be unveiled through the analysis of used engine oils throughout their service life. , The adjustment of engine oil replacement frequency may be necessary based on operating conditions, such as urban settings, and other factors accelerating oil aging and part wear. , Therefore, the frequency of engine oil analysis may need to be adjusted as diesel engine age, ensuring regular analysis to identify issues for maintaining reliability and performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Analysis of Soft Computing Models for Predicting Viscosity in Diesel Engine Lubricants: An Alternative Approach to Condition Monitoring

Pourramezan,

Rohani,

Abbaspour-Fard

2023

ACS Omega

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The viability of employing soft computing models for predicting the viscosity of engine lubricants is assessed in this paper. The dataset comprises 555 reports on engine oil analysis, involving two oil types (15W40 and 20W50). The methodology involves the development and evaluation of six distinct models (SVM, ANFIS, GPR, MLR, MLP, and RBF) to predict viscosity based on oil analysis results, incorporating metallic and nonmetallic elements and engine working hours. The primary findings indicate that the radial basis function (RBF) model excels in accuracy, consistency, and generalizability compared with other models. Specifically, a root mean square error (RMSE) of 0.20 and an efficiency (EF) of 0.99 were achieved during training and a RMSE of 0.11 and an EF of 1 during testing, utilizing a 35-network topology and an 80/20 data split. The model demonstrated no significant differences between actual and predicted datasets for average and distribution indices (with P-values of 1.00). Additionally, robust generalizability was exhibited across various training sizes (ranging from 50 to 80%), attaining a RMSE between 0.09 and 0.20, a mean absolute percentage error between 0.23 and 0.43, and an EF of 0.99. This study provides valuable insights for optimizing and implementing machine learning models in predicting the viscosity of engine lubricants. Limitations include the dataset size, potentially affecting the generalizability of findings, and the omission of other factors impacting engine performance. Nevertheless, this study establishes groundwork for future research on the application of soft computing tools in engine oil analysis and condition monitoring.

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Section: Introductionmentioning

confidence: 99%