1. A ǂFramework for applying data-driven AI/ML models in reliabilityRok Hribar, Margarita Antoniou, Gregor Papa, 2024, samostojni znanstveni sestavek ali poglavje v monografski publikaciji Povzetek: In this chapter, we present a framework for applying artificial intelligence (AI)/machine learning (ML) in reliability, in the context of the iRel40 project. Data-driven models are becoming an increasingly fruitful tool for detecting patterns in complex data and identifying the circumstances in which they occur. Using only data, gathered along the value chain, data-driven methods are now being used to detect indications of potential early failures, signs of wear out or degradation, and other unwanted events within the development, fabrication, or service phases of the electronic components and systems. We present general considerations that were found to be important during the iRel40 project, when designing pipelines that combine data processing with the AI/ML models for predicting or detecting reliability issues. This chapter serves as an introduction to the definitions and concepts used within the specific use cases that rely on the AI/ML methodology within the iRel40 project.
Ključne besede: machine learning, artificial intelligence, data-driven models
Objavljeno v DiRROS: 23.07.2024; Ogledov: 46; Prenosov: 15
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2. Reliability improvements for in-wheel motorGašper Petelin, Rok Hribar, Stane Ciglarič, Jernej Herman, Anton Biasizzo, Peter Korošec, Gregor Papa, 2024, samostojni znanstveni sestavek ali poglavje v monografski publikaciji Povzetek: Setting up a reliable electric propulsion system in the automotive sector requires an intelligent condition monitoring device capable of reliably assessing the state and the health of the electric motor. To allow for a massive integration of such monitoring devices, they must be inexpensive and small. These requirements limit their accuracy. However, we show in this chapter that these limitations can be significantly reduced by appropriate processing of the sensor data. We have used machine learning models (random forest and XGBoost) to transform very noisy motor winding insulation resistance measurements made by a low-cost device into a much more reliable value that can compete with measurements made by a high-priced state-of-the-art measurement system. The proposed method is an important building block for a future smart condition monitoring system and enables a cost-effective and accurate assessment of the condition of electric motor health in connection with the condition of their winding insulation.
Ključne besede: machine learning models, low-cost device, electric motor
Objavljeno v DiRROS: 23.07.2024; Ogledov: 56; Prenosov: 16
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3. Explainable anomaly detection of 12-Lead ECG signals using denoising autoencoderRok Hribar, Drago Torkar, 2024, samostojni znanstveni sestavek ali poglavje v monografski publikaciji Povzetek: The basic idea was to create a proof of concept demonstrating that the indoor navigation and localisation is possible using only passive tags. For this purpose, a smartphone navigation app was developed to be used in inner parts of the buildings and which can operate with no satellite positioning service available and no communication network present. The satellite navigation systems (GPS, GLONASS, Galileo, BeiDou, QZSS, IRNSS) inside buildings, at least on lower floors and cellars, in central parts, and away from windows, usually do not work, or their accuracy is very reduced due to a small number of visible satellites. The communication networks (WiFi, LTE, 5G…) might not be available in some circumstances such as catastrophic incidents, power reductions, or similar, which disables the localisation systems based on them. The purpose of the smartphone app was twofold. First, to develop a reliable, simple-to-use, and cheap indoor navigation system that could be used in large buildings like hospitals, shopping malls, trade centres, fairs, etc. where no other positioning service is available. Second, to develop an indoor position reporting system that can be used in accidents and mass-casualty incidents for reporting triage decisions to the server. Both functionalities are based on the QR codes [1] holding all the information needed.
Ključne besede: internet thinks, indoor navigation, systems, QR codes
Objavljeno v DiRROS: 02.07.2024; Ogledov: 106; Prenosov: 42
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4. Models for forecasting the traffic flow within the city of LjubljanaGašper Petelin, Rok Hribar, Gregor Papa, 2023, izvirni znanstveni članek Povzetek: Efficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, accurately modeling complex spatiotemporal dependencies can be a difficult task, especially when real-time data collection is not possible. This study aims to tackle this challenge by proposing a solution that incorporates extensive feature engineering to combine historical traffic patterns with covariates such as weather data and public holidays. The proposed approach is assessed using a new real-world data set of traffic patterns collected in Ljubljana, Slovenia. The constructed models are evaluated for their accuracy and hyperparameter sensitivity, providing insights into their performance. By providing practical solutions for real-world scenarios, the proposed approach offers an effective means to improve traffic flow prediction without relying on real-time data.
Ključne besede: traffic modeling, time-series forecasting, traffic-count data set, machine learning, model comparison
Objavljeno v DiRROS: 28.09.2023; Ogledov: 511; Prenosov: 229
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7. A comparison of models for forecasting the residential natural gas demand of an urban areaRok Hribar, Primož Potočnik, Jurij Šilc, Gregor Papa, 2019, izvirni znanstveni članek Povzetek: Forecasting the residential natural gas demand for large groups of buildings is extremely important for efficient logistics in the energy sector. In this paper different forecast models for residential natural gas demand of an urban area were implemented and compared. The models forecast gas demand with hourly resolution up to 60 h into the future. The model forecasts are based on past temperatures, forecasted temperatures and time variables, which include markers for holidays and other occasional events. The models were trained and tested on gas-consumption data gathered in the city of Ljubljana, Slovenia. Machine-learning models were considered, such as linear regression, kernel machine and artificial neural network. Additionally, empirical models were developed based on data analysis. Two most accurate models were found to be recurrent neural network and linear regression model. In realistic setting such trained models can be used in conjunction with a weather-forecasting service to generate forecasts for future gas demand.
Ključne besede: demand forecasting, buildings, energy modeling, forecast accuracy, machine learning
Objavljeno v DiRROS: 15.03.2019; Ogledov: 2434; Prenosov: 1119
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