Contact Dr Agusmian Ompusunggu
- Tel: +44 (0) 1234 754120
- Email: Agusmian.Ompusunggu@cranfield.ac.uk
- ORCID
- Google Scholar
- ResearchGate
Areas of expertise
- Computing, Simulation & Modelling
- Instrumentation, Sensors and Measurement Science
- Through-life Engineering Services
Background
Agusmian Partogi Ompusunggu is an experienced engineer with industrial backgrounds and with broad technical expertise in the areas of (i) Maintenance Systems, (ii) Prognostics and Health Management (PHM) Systems Development, (iii) Smart Sensing/ IoT for Condition & In-Process Monitoring, (iv) Dynamics/Vibration Analysis of Rotating Machines, (v) Industrial Data Analytics/Applied Artificial Intelligence (AI), (vi) Contact Mechanics & Tribology, and (vii) Additive Manufacturing.
He holds PhD in condition monitoring and prognostics of automotive critical components from KU Leuven, Belgium. He earns BSc and MSc both in Mechanical Engineering from Institute of Technology Bandung (ITB), Indonesia. His BSc thesis was focused on designing, manufacturing and balancing of flexible rotor-shaft systems, while his MSc thesis was focused on error analysis of a frequency response function (FRF) obtained from an impact hammer testing.
Dr Ompusunggu joined Cranfield in February 2022 as a Senior Lecturer in Maintenance and IoT. Prior to joining Cranfield University, he was a senior research engineer and project leader at Flanders Make vzw, Belgium, an R&D centre bridging academic and industrial researches. Therein, Dr Ompusunggu performed industry-driven research and development (R&D) projects in collaboration with local and abroad research institutes, as well as various high tech multi-national companies. Besides, he led R&D projects with different teams consisting of experienced research & application engineers with MSc or/and PhD degree.
Research opportunities
I am currently recruiting for PhD students and post-docs in three main areas of (1) Maintenance Systems for achieving zero-downtime of high-value critical assets, (2) Zero-defect manufacturing, and (3) Circular economy.
Sub-areas of interest are (i) Value-driven maintenance & asset management strategy, (ii) IoT sensors/devices & data analytics algorithms for condition monitoring & manufacturing (in-process) monitoring, (iii) Prognostics and Health Management (PHM) systems & digital twin for Predictive Maintenance (PdM) of high value assets, (iv) Physics of failure of critical components/ (sub)-systems and its manifestation to the system's responses, (v) Vibration signal analysis of rotating machines, (vi) Physics-informed machine learning algorithm development for maintenance or other engineering applications.
Do not hesitate to get in contact if you are interested in pursuing a PhD or doing further a post-doc research in one of those areas.
Current activities
Dr Ompusunggu is a Senior Lecturer in Maintenance and IoT, based in the Centre for Life-cycle Engineering and Management (CLEM). Currently, he is leading two course modules, namely (i) Maintenance Planning and Control, and (ii) Through-life Business Models & Servitisation. Besides, he has been involved in supervising of several group projects and MSc Theses.Clients
- European Commission
- Nestle
Publications
Articles In Journals
- Liu H, Rahman M, Rahimi M, Starr A, Durazo-Cardenas I, Ruiz-Carcel C, Ompusunggu A, Hall A & Anderson R (2023) An autonomous rail-road amphibious robotic system for railway maintenance using sensor fusion and mobile manipulator, Computers and Electrical Engineering, 110 (September) Article No. 108874.
- Tod G, Ompusunggu AP & Hostens E (2023) An improved first-principle model of AC powered solenoid operated valves for maintenance applications, ISA Transactions, 135 (April) 551-566.
- Maulana F, Starr A & Ompusunggu AP (2023) Explainable data-driven method combined with Bayesian filtering for remaining useful lifetime prediction of aircraft engines using NASA CMAPSS datasets, Machines, 11 (2) Article No. 163.
- Brijder R, Helsen S & Ompusunggu AP (2023) Switching Kalman filtering-based corrosion detection and prognostics for offshore wind-turbine structures, Wind, 3 (1) 1-13.
- Brijder R, Hagen CHM, Cortes A, Irizar A, Thibbotuwa UC, Helsen S, Vasquez S & Ompusunggu AP (2022) Review of corrosion monitoring and prognostics in offshore wind turbine structures: current status and feasible approaches, Frontiers in Energy Research, 10 (September) Article No. 991343.
- Vasquez S, Verhelst J, Brijder R & Ompusunggu AP (2022) Detection, prognosis and decision support tool for offshore wind turbine structures, Wind, 2 (4) 747-765.
- Thanki A, Goossens L, Ompusunggu AP, Bayat M, Bey-Temsamani A, Van Hooreweder B, Kruth J-P & Witvrouw A (2022) Melt pool feature analysis using a high-speed coaxial monitoring system for laser powder bed fusion of Ti-6Al-4 V grade 23, The International Journal of Advanced Manufacturing Technology, Available online 12 April 2022 (9-10).
- Avendano DN, Vandermoortele N, Soete C, Moens P, Ompusunggu AP, Deschrijver D & Van Hoecke S (2022) A semi-supervised approach with monotonic constraints for improved remaining useful life estimation, Sensors, 22 (4) Article No. 1590.
- Verhelst J, Coudron I & Ompusunggu AP (2022) SCADA-compatible and scaleable visualization tool for corrosion monitoring of offshore wind turbine structures, Applied Sciences, 12 (3) Article No. 1762.
- Ompusunggu AP, Vonderscher Y & Motl D (2021) Physics-based vibration feature for detecting eccentric workpiece/runout faults during continuous generating gear grinding processes, Mechanical Systems and Signal Processing, 153 (May) Article No. 107536.
- Laroche L, Li X, Helsen J, Lacaze F, Melot A, Ompusunggu A, Liu C, Mauricio A, Peeters C, Perez M, Paillot G, Passos S, Smith WA, Thomas X, Qi J, Sierra-Alonso EF, Leclère Q, André H, Benaïcha Y, Anastasio D, Birem M, Billon K, Chin ZY, Bonnardot F, Daems PJ, Combet F, De Geest R, Daga AP, Griffaton J, Elyousfi B, Hawwari Y & Gryllias K (2021) Using a smartphone camera to analyse rotating and vibrating systems: Feedback on the SURVISHNO 2019 contest, Mechanical Systems and Signal Processing, 154 (June) Article No. 107553.
- Ompusunggu A, Devos S & Petre F (2020) Stochastic-resonance based fault diagnosis for rolling element bearings subjected to low rotational speed, International Journal of Prognostics and Health Management, 4 (2).
- Barbini L, Ompusunggu A, Hillis A, du Bois J & Bartic A (2017) Phase editing as a signal pre-processing step for automated bearing fault detection, Mechanical Systems and Signal Processing, 91 (July) 407-421.
- Ompusunggu AP, Sas P & Van Brussel H (2015) Distinguishing the effects of adhesive wear and thermal degradation on the tribological characteristics of paper-based friction materials under dry environment: A theoretical study, Tribology International, 84 (April) 9-21.
- Ompusunggu A, Papy J & Vandenplas S (2015) Kalman-filtering-based prognostics for automatic transmission clutches, IEEE/ASME Transactions on Mechatronics, 21 (1) 419-430.
- Ompusunggu AP (2014) On the derivation of the pre-lockup feature based condition monitoring method for automatic transmission clutches, Mechanical Systems and Signal Processing, 46 (1) 114-128.
- Ompusunggu AP, Papy JM, Vandenplas S, Sas P & Van Brussel H (2013) A novel monitoring method of wet friction clutches based on the post-lockup torsional vibration signal, Mechanical Systems and Signal Processing, 35 (1-2) 345-368.
- Ompusunggu AP, Sas P & Van Brussel H (2013) Modeling and simulation of the engagement dynamics of a wet friction clutch system subjected to degradation: An application to condition monitoring and prognostics, Mechatronics, 23 (6) 700-712.
Conference Papers
- Ompusunggu AP (2023) Systematic methodology for generating natural spall faults on rolling element bearings. In: 5th International Conference on Maintenance, Condition Monitoring and Diagnostics (MCMD 2021), Online, 16-18 February 2021.
- Ompusunggu AP & Ruiz Carcel C (2023) Low-cost vibration sensor with low frequency resonance for condition monitoring of low speed bearings: a feasibility study. In: 1st Workshop on Low-Cost Digital Solutions for Industrial Automation, 2023, Cambridge, 25-26 September 2023.
- Ompusunggu AP & Hostens E (2023) Physics-inspired feature engineering for condition monitoring of alternating current-powered solenoid-operated valves. In: 5th International Conference on Maintenance, Condition Monitoring and Diagnostics (MCMD 2021), Online, 16-18 February 2021.
- Brijder R, Helsen S & Ompusunggu AP (2022) Corrosion prognostics for offshore wind-turbine structures using Bayesian filtering with bi-modal and linear degradation models. In: 10th European Workshop on Structural Health Monitoring (EWSHM), Palermo, 4-7 July 2022.
- Tod G, Ompusunggu AP, Struyf G, Pipeleers G, Grave KD & Hostens E (2021) Physics-Informed Neural Networks (PINNs) for improving a thermal model in stereolithography applications. In: 54th CIRP Conference on Manufacturing Systems 2021 (CIRP CMS 2021), Virtual Event, 22-24 September 2021.
- Ompusunggu AP, Eryilmaz K & Janssen K (2021) Condition monitoring of critical industrial assets using high performing low-cost MEMS accelerometers. In: 54th CIRP Conference on Manufacturing Systems 2021 (CIRP CMS 2021), Virtual Event, 22-24 September 2021.
- Yudanto R, Ompusunggu A & Bey-Temsamani A (2015) On improving low-cost IMU performance for online trajectory estimation. In: SPIE Microtechnologies, Barcelona, 4-6 May 2016.
- Ompusunggu A, Janssens T, Al-Bender F, Sas P, Van Brussel H & Vandenplas S (2010) Contact stiffness characteristics of a paper-based wet clutch at different degradation levels. In: 17th International Colloquium Tribology (TAE2010), Stuttgart.