Smart Tribology Training Kit for Real-Time Evaluation of Lubricant Quality based on Temperature and Noise Characteristics under Variable Loading Conditions

¹ Universitas Negeri Jakarta, Department of Mechanical Engineering Education, 13220 East Jakarta, Indonesia
² Universitas Sultan Ageng Tirtayasa, Department of Mechanical Engineering Vocational Education, 42117 Serang City, Indonesia
³ Abai Kazakh National Pedagogical University, Department of Pedagogy and Psychology, 50010 Almaty, Kazakhstan

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Published online: 2 July 2026

Abstract

Degradation of lubricating oil is one major contributor to the increased temperature, noise and wear on mechanical components. Conventional lubricant testing is generally based on expensive laboratory analysis and does not reflect the friction phenomenon in real-time, thus limiting the understanding of the lubricant performance especially for the context of vocational education. This research developed a Smart Tribology Training Kit to assess the quality of lubricants by real-time temperature and noise monitoring under different friction conditions. The research adopts an experimental engineering methodology including requirements analysis, design, element analysis (FEA), fabrication and performance testing. Experiments were conducted with load variations of 2, 6 and 11 kg at rotational speeds of 981 rpm and 1489 rpm under three lubrication conditions viz. oilless, SAE 10W-30 used oil and SAE 10W-30 new oil. The structural analysis was performed and the results were .014 mm of maximum displacement and 1.208 as safety factor, confirming the operational safety in the test range used. The performance tests showed that the lubrication-free conditions had the highest temperatures and noise, while the new oils reduced both parameters consistently. For the oilless condition, the 1489 rpm with 11 kg load generates 35.7°C and 99.9 dBA, whereas the new oil condition generates 32.2°C and 89.2 dBA. Pearson’s correlation analysis showed a very strong and statistically significant relationship between temperature and noise across test conditions (r = 0.934, p < 0.001) confirming the consistency of the dual-sensor measurement approach.