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Predictive Maintenance of Electric Motor-System Using Vibration Analysis
(German-Mongolian Institute for Resources and Technology, 2026-05-01) Boldbaatar, Tuvshinbayar; Supervisor 1: Prof. Lee, Sungchil; Supervisor 2; Prof. Kim, Young Suk
Electric motors are widely used in industrial and mining operations, where their reliability directly affects productivity, safety, and maintenance costs. Unexpected failures can lead to significant downtime, making predictive maintenance an important strategy for improving system performance. This study investigates using vibration analysis to detect common mechanical faults in electric motors, including bearing defects, misalignment, and unbalance. Publicly available datasets, including the Case Western Reserve University (CWRU) bearing dataset and a belt-driven rotating shaft system dataset from the Mendeley Data repository, are used to analyze vibration signals under both normal and faulty conditions. For bearing fault detection, we compare signals from healthy and defective cases using time-domain, frequency-domain, and envelope analysis, allowing us to examine both general vibration behavior and high-frequency impacts. The data are sampled at 12 kHz, with the motor operating at 1797 rpm, enabling identification of localized inner race defects. In addition, vibration data from a belt-driven rotating shaft system are used to study unbalance and misalignment. Normal operation is compared separately with each fault condition using time-domain and frequency-domain methods. The shaft operates at 1238 rpm, and Fast Fourier Transform (FFT) analysis focuses on the shaft rotational frequency (1X) and its harmonics (2X and 3X) for fault identification. The results show that different fault types produce distinct vibration patterns, though the clarity of detection depends on sensor placement and measurement direction, allowing clear fault differentiation. A combination of time-domain features, harmonic analysis, and envelope methods provides a more reliable diagnosis than using a single parameter. Overall, this study demonstrates that vibration analysis can effectively detect both motor-related and externally induced faults, supporting early fault detection and improving predictive maintenance strategies.
Collaborative Approach in Research Project Management
(German-Mongolian Institute for Resources and Technology, 2026-05-28) Temuulen Boldbaatar; 1 st Supervisor: Prof.Dr. Enkhzaya Chuluunbaatar; 2 st Supervisor: Prof.Dr. Bolormaa Purevdorj
This thesis examines how collaborative management functioned in a small multidisciplinary research project, using the "Towards Sustainable Mining in Mongolia" (ToSMM) project as a case study. The project, carried out by a 12-member team of students, professional experts, and academic supervisors, aimed to develop a localized TSM Mongolia Guidebook for engineers and mining professionals under constraints such as stakeholder availability, data access, and delayed fieldwork. Drawing on collaborative governance and project management theory, the study operationalizes collaboration through seven analytical dimensions: communication, coordination, role clarity, participation and feedback, workflow continuity, mutual support, and collaborative learning. A single-case mixed-methods design was applied, combining a survey (10 valid responses from 12 team members), five semi-structured interviews, and a review of project documents including the Project Charter and Work Breakdown Structure, with findings triangulated across sources. The results show that collaboration operated as a structured management mechanism linking formal project design with adaptive implementation. Role allocation, weekly meetings, feedback discussions, internal review, and task adjustment helped the team maintain progress during external disruption, while reciprocal learning developed among students, experts, and supervisors. The study also identifies tensions, including time pressure, hidden coordination costs, and uneven participation, indicating that effective collaboration requires continuous facilitation. Practical recommendations are offered for clear roles, structured meetings, supportive feedback, early team building, inclusive participation, and alternative workflows in future multidisciplinary research projects.
Numerical Modeling Study on the Effects of Urban Greening on Microclimate
(German-Mongolian Institute for Resources and Technology, 2026-05) Erdenechuluun, Ariuntsetseg; 1st Supervisor: Prof. Dr. Gantuya Ganbat; 2nd Supervisor: Prof. Dr. Jambajamts Lkhamjav
Urban greening is widely promoted as a nature-based remedy for urban overheating, yet its microclimate response in cold, semi-arid, mountain-bounded capital cities such as Ulaanbaatar differs fundamentally from the linear cooling reported in humid climates. The Weather Research and Forecasting model coupled to the Single-Layer Urban Canopy Model was configured in four nested domains down to 1 kilometer over Ulaanbaatar, driven by ERA5 reanalysis. Four scenarios were simulated for 22 June 2025 by modifying the urban impervious-area fraction (FRC_URB) at 1.0, 0.9, 0.5, and 0.1, corresponding to greening levels of 0, 10, 50, and 90 percent. The 0.9 case was retained as the control. The control reproduced the tropospheric temperature profile with a root-mean-square error of 1.2°C and a nocturnal urban heat island of 2.5°C, matching the long-term observed summer mean. The daytime response was moisture-dominated: a shift from 0 to 90 percent greening lowered the 2 m air temperature by only 0.3°C but raised the surface dew point by 1.8°C. The nocturnal urban heat island declined monotonically with greening fraction and reversed sign at the strongly greened limit, falling from 3.5°C through 0.6°C at moderate greening to −2.9°C at 90 percent greening, driven by reduced heat-storage release and weakened surface roughness that allowed katabatic drainage to ventilate the basin. The −2.9°C value is an upper bound sensitive to the model soil-moisture treatment. Intermediate rather than maximum greening minimizes the nocturnal urban heat island in a basin-confined city, and the full cooling potential is realized only when greening is combined with irrigation, permeable surfaces, and rainwater harvesting.
Optimization and Evaluation of Operational Performance for Membrane Bioreactor (MBR)
(German-Mongolian Institute for Resources and Technology, 2026-05) Dagdangonchig, Undarmaa; 1 st Supervisor: Dr. Ariuntuya Tserendorj; 2 nd Supervisor; Dr. Tamir Puntsag; Advisor: Ms. Dagiisuren Badamkhatan
This study evaluates the operational performance of a membrane bioreactor (MBR) system at the Khaanzaa Wastewater Treatment Plant (WWTP), operated under subcontract with the Oyu Tolgoi mining project in the Gobi region of Mongolia. The assessment is based on laboratory experiments and statistical analysis of long-term operational data collected between 2024 and 2025. The results demonstrate that the MBR system achieves high treatment efficiency, with effective removal of chemical oxygen demand (COD) and ammonia under the studied conditions. A key finding is the strong inverse relationship between sludge wasting rate (Qw) and sludge retention time (SRT). Insufficient or irregular sludge wasting resulted in excessively high SRT, leading to aged biomass, increasing MLSS, poor sludge settleability, and unstable process performance. Statistical analysis further showed significant variability in MLSS and dissolved oxygen (DO), with frequent deviations from optimal ranges. Excessive DO levels contributed to unnecessary energy consumption, while low DO conditions risked reduced nitrification efficiency. Membrane performance analysis demonstrated that permeability was strongly affected by fouling and operational conditions. Although periodic cleaning temporarily restored performance, rapid fouling recurrence indicated the need for more stable biological control. Based on these findings, optimization of sludge wasting and aeration control is recommended to maintain stable MLSS and SRT conditions. Maintaining an SRT of approximately 20 days and DO within the optimal range (1.5–2.5 mg/L) is essential for improving system stability, reducing fouling, and enhancing long-term MBR performance.
Temporal and Spatial Trends of Forest Fires and Carbon Dynamics in Tujiin Nars
(German-Mongolian Institute for Resources and Technology, 2026-06) Enkhjargal B.; 1 st Supervisor/Examiner: Prof. Dr. Gantuya Ganbat; 2 nd Supervisor/Examiner: Dr. Batsaikhan Vanchindorj; Advisor: Ms. Enkhjin Enkhbold
Wildfires are one of the most important disturbances in boreal and forest steppe ecosystems, and are of great importance to biodiversity, carbon cycling and ecosystem resistance. In this study, a spatiotemporal analysis of wildfire dynamics, risks, emissions and post-fire carbon uptake in Selenge Province and the Tujiin Nars State Special Protected Area of northern Mongolia from 2001–2025 were performed. Wildfire occurrence and area burned are derived from the MCD64A1 burned area product processed by Google Earth Engine with verification data from the National Emergency Management Agency (NEMA) of Mongolia. LST, precipitation, wind speed, NDVI, slope, elevation and aspect were incorporated for fire risk and carbon calculation. During the period of this study (2001-2025) 605 fire detections and 7,809.29 km² of burned area are detected in Selenge Province with maximum in 2009 (1,566.46 km²) which shows peak fire year. Fire season is dominantly in Spring (March-May, 53% of total area burned) followed by Summer (37%). While in Tujiin Nars, fire occurs in three years of 2005, 2007 and 2025 with maximum burned area occurring in 2005 (83.633 km²) followed by 2025 (41.383 km²) and 2007 (36.684 km²). Most fires were observed in north and northeast of the protected area, which coincides with distribution of forest steppe vegetation and environmental gradients. Wildfire risk was evaluated using multi criteria Wildfire Risk Index (RI) considering combustibility index, topomorphological index, human occupation index, and MaxEnt approach modeling species distribution. For provincial scale, prediction performance of MaxEnt model is highly satisfactory; LST becomes dominant factor in May (43.22%), and wind speed in October (42.88%), while precipitation is consistently important throughout the study season. Locally in Tujiin Nars the main contributing environmental variables controlling fire ignition varied with fire years, moisture and topographic factors controlled the fire in 2005 fire season; extreme thermal environment was a direct driver in 2007 fire season; while wind becomes the direct dominant driving factor for fire in 2025 fire season, which contributes 62.81% of the model and has permutation importance of 72.41%. All of the MaxEnt models have high performance in predicting fire occurrences with AUC values ranged from 0.836 to 0.966. The calculation of CO2 emission from the fire events in Tujiin Nars was based on IPCC 2006 Guidelines, and despite that the cumulative burned area is mainly grasslands/steppes (160.37 km² of 166.39 km² in total, which accounts for 96.38% of total burned area), forest fire contribution to total CO2 emission was 88.8% (5,706.96 out of 6,426.57 tons), which was 2,000,000 kg/km² of fuel load compared with grassland (2,940 kg/km²) of fire. And, the results of ecosystem recovery and carbon sequestration over years (from 2005 to 2025 based on MODIS GPP data) suggest a great recovery potential of Tujiin Nars ecosystem, where mean C sequestration over year increases from 4.80 t C/ha to 6.26 t C/ha, and the total sequestration increased from 340,251.89 ton to 443,327.84 ton in 2025. These findings highlight the importance of fire prevention and ecosystem-based management for the carbon sequestration function and integrity of the Tujiin Nars State Special Protected Area under the context of climate change.