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All Journal AUSTENIT Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Prosiding SNTTM TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Sholahudin
Departement Of Mechanical Engineering, Universitas Indonesia, Depok, West Java, 16424, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Brake failure and fault detection in heavy vehicles : trends, challenges, and research gap from a systematic literature review Sepriyanto; Danardono A Sumarsono; Mohammad Adhitya; Sholahudin
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 13 No 2 (2026): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK- Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v13i2.2164

Abstract

Brake failure in heavy vehicles, particularly trucks and buses, continues to be a major contributor to severe traffic accidents, especially on long downhill routes where braking systems are subjected to sustained thermal and mechanical loads. Although numerous approaches for brake fault detection and diagnosis have been proposed over the past decade, most studies remain confined to laboratory environments or simulation-based analyses and predominantly target passenger vehicles. This systematic literature review provides a critical examination of existing research on brake failure and fault detection, with a specific focus on heavy vehicle applications. A structured search of the Scopus database was conducted using the keywords (“brake failure” OR “brake fault”). AND vehicle*, covering publications from 2016 to 2025. Following the PRISMA framework, 32 relevant articles were selected from an initial set of 134 records. The selected studies were analyzed in terms of methodological approaches, key findings, reported limitations, and research contributions. The results reveal a notable increase in research activity since 2020, driven mainly by sensor-based techniques, physical modeling, and artificial intelligence–based methods. However, studies explicitly addressing heavy vehicles and validated under real-road operating conditions remain limited. This review synthesizes current research trends, clarifies unresolved gaps, and outlines future research directions, particularly in the areas of multi-sensor data integration, intelligent diagnostic algorithms, and Digital Twin–enabled predictive maintenance, to enhance brake failure detection and overall heavy vehicle safety.
MULTI-OBJECTIVE OPTIMIZATION OF TAF VENTILATION SYSTEMS WITH DIFFERENT INLET GEOMETRIES FOR PARTICLE CONTROL AND THERMAL COMFORT IN OPERATING ROOMS Ghiffar Yanuar; Sholahudin; Kurniawan Teguh Waskito; Ridho Irwansyah
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 6 No. 5 (2026): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.22006312

Abstract

Operating room ventilation plays an important role in controlling bacteria-carrying particles (BCP), reducing infection risk, and maintaining thermal comfort for medical staff. Temperature-controlled airflow (TAF) is a promising ventilation strategy because it supplies air through central and peripheral inlets with different temperatures to form a stable downward airflow. This study aims to optimize a TAF ventilation system by considering BCP concentration, energy consumption, and predicted mean vote (PMV). A three-dimensional operating room model was developed using computational fluid dynamics (CFD). Four operating parameters were varied using central composite design (CCD), while the peripheral inlet position was evaluated separately as a two-level geometry parameter. Airflow was solved using the RNG k–ε turbulence model, while BCP motion was modeled using the discrete phase model (DPM). CFD simulation results were used to train an artificial neural network (ANN) model, which was then integrated with a multi-objective genetic algorithm (MOGA). TOPSIS was used to select the best compromise solution from the Pareto front. The optimum condition was obtained at a central inlet velocity of 0.2735 m/s, peripheral inlet velocity of 0.1671 m/s, central inlet temperature of 21.8649 °C, peripheral inlet temperature of 22.0127 °C, and peripheral-to-central inlet distance of 2.0474 m. This configuration produced a BCP concentration of 0.9115 CFU/m³, energy consumption of 9.8832 kW, and PMV of −0.3186. These results indicate that the modified TAF configuration can produce low particle concentration, relatively low energy consumption, and acceptable thermal comfort in the operating room.
Prediksi beban pendinginan menggunakan Machine Learning dan parameter data cuaca Sholahudin; Nazwan Hafiz Firdaus; Mrinal Bhowmik
AUSTENIT Vol. 17 No. 2 (2025): AUSTENIT: Oktober 2025
Publisher : Politeknik Negeri Sriwijaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/austenit.v17i2.11332

Abstract

Cooling systems account for a substantial amount of end-use energy consumption in building sector. This system is responsible for removing the heat from the building to maintain the indoor temperature at a certain comfort level standard. Prediction of cooling load in a building is useful to design the HVAC system operation and energy management efficiently. This paper presents a method for predicting instantaneous building cooling load, relying on the inputs extracted from weather data and artificial neural networks. The data sets are generated by simulating cooling load at an educational building located in Indonesia for one year using Energy Plus software. The input parameters include dry-bulb temperature, relative humidity, wind speed, wind direction, horizontal infrared radiation rate, diffuse solar radiation rate, and direct solar radiation rate. Analysis of variance and Pearson coefficient of correlation was applied to analyze the relative contribution of individual input parameters on the cooling load. Both methods have consistently shown that the dry bulb temperature is the most influential parameters, while wind speed and wind direction have less significant effect on cooling loads. The result of this study indicates that the optimized ANN model with selected input parameters has successfully predicted the cooling load with coefficient of variation (CV) of 15.26%.
Thermal performance enhancement in electric motor rotors: Evaluating the impact of rotating heat pipes Khairu Rezqi; Nandy Putra; Sholahudin Sholahudin; Gandjar Kiswanto
Prosiding SNTTM Vol 23 No 1 (2025): SNTTM XXIII October 2025
Publisher : BKS-TM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71452/hdn06j82

Abstract

Effective thermal management is essential in high-performance electric motors, where rotor overheating accelerates demagnetization and reduces operational lifespan. This study investigates a horizontally mounted, wick based rotating heat pipe (RHP) as a passive cooling solution for an induction motor rotor. The system was subjected to stepped heat loads 20, 25, 30, and 35 W, also and speeds 0, 250, 500, 750 RPM, with time resolved measurements acquired to evaluate steady state thermal resistance (Rth) and transient response. The results reveal a non monotonic relationship between rotational speed and thermal performance. Contrary to initial assumptions, the RHP achieved its lowest Rth of 0.164 °C/W not at standstill, but at a moderate speed of 250 RPM. This performance peak is attributed to a balanced interplay where gentle centrifugal force enhances capillary-driven liquid distribution, maximizing effective evaporation without inducing flow instability. Compared to the solid rotor baseline, the RHP consistently reduced rotor temperatures by up to 6 °C and lowered thermal resistance by more than 70%. Additionally, the RHP halved the thermal time constant following each power step, indicating superior transient regulation. The identification of an optimal rotational speed window, distinct from any transitional instability zone, offers critical design insight for embedding RHPs in next-generation electric machines where spatial constraints and thermal reliability are paramount.
Co-Authors Danardono A Sumarsono Gandjar Kiswanto Ghiffar Yanuar Khairu Rezqi Mohammad Adhitya, Mohammad Mrinal Bhowmik Nazwan Hafiz Firdaus Putra, Nandy Setiadi Djaya Ridho Irwansyah Sepriyanto Waskito, Kurniawan Teguh