Energy

Industrial machines are continuously evolving towards more complex systems, often featuring multiple interacting subsystems and components. These assets are often operating in complex industrial environments, influenced by a variety of different factors in interaction with a human end user (e.g. the operator). Since failures or downtime can have serious consequences (e.g., with respect to safety or economic impact), it is important to adequately monitor the health condition of these machines. Not in the least, this holds for wind turbines, for which the operational management not only is expensive, but often also crucial for a stable power supply. Therefore, modern wind turbines are equipped with sensors. The data from these sensors is analyzed to predict abnormal behavior and in this way enable to plan maintenance ahead of time (predictive maintenance).

Prognostics and Health Management (PHM) is the research field that links studies of failure mechanisms of industrial assets to system lifecycle management. The goal of Prognostics and Health Management (PHM) is to provide methods and tools to design optimal maintenance policies for a specific asset under its distinct operating and degradation conditions, achieving a high availability at minimal costs.

The research within the Flanders AI Research Program

The increasing complexity of industrial assets and their operating environments increasingly requires a more real-time and dynamic health assessment.  Traditional PHM approaches, which are primarily knowledge-based, no longer suffice. Therefore, the research groups in this use case study how data-driven and knowledge-based models can reinforce one another by combining them in a hybrid methodology to perform more accurate prognostics and management of ageing industrial machinery. This includes research towards improved techniques for

• Diagnostics, for characterizing the health state of the machine, in function of its degradation
• Prognostics, for monitoring the evolution of the health state & the degradation process, towards remaining useful life prediction
• Decision support, for providing stakeholder-centric insights w.r.t. aspects in a machine’s health state that influence its remaining lifetime.

Challenges and researched methods

Analyzing (sensor) data from such complex industrial machinery comes along with several specific challenges that form the subject of this use case. These include amongst others the lack of labelled data on failures and machine degradation, due to which un- and semisupervised techniques are being studied in combination with transfer learning to exploit the limited available labelled data in the best possible way. In some cases, it might be important to collect feedback on particular aspects from the user, for example to learn about the subtle difference between normal and anomalous behavior, for which the use of active learning techniques will be researched. Also the translation of the results of the algorithms into interpretable insights for decision support is crucial. The biggest challenge and main focus of the use case is however combining data-driven and knowledge-based models, such that they can reinforce one another in a hybrid learning strategy.

Results

The first phase of this research was focused on data-driven techniques for diagnostics and prognostics. Amongst others, a methodology was developed to assess the condition of a machine based on data-driven health indicators. These indicators can also serve as the basis to detect the degradation in performance of an asset, in which the different operating modes of a machine are accounted for. To tackle the aforementioned challenge of a lack of labelled data, also a number of transfer learning approaches have been researched, applied to the problem of detecting icing on the blades of a wind turbine. Furthermore, also the first steps were made towards a data-driven digital twin for hybrid PHM.

A number of these topics are also being researched in more depth in the AI ICON projects CONSCIOUS and TRACY, that were recently started in collaboration with research partners from the Flanders AI Research program and a consortium of Flemish companies. A short description of these projects can be found below.

Connect

• Contact person:  Alessandro Murgia  (Data and AI Completence Lab (EluciDATA Lab) - Sirris), Elena Tsiporkova (Data and AI Completence Lab (EluciDATA Lab) - Sirris)
• Involved research groups: Sirris (Data and AI Completence Lab (EluciDATA Lab)), KU Leuven (DTAI, STADIUS, LMSD), UGent (IDLab, EEDT, GAIM, KERMIT) en UHasselt (QM)

More info and related projects

• The CONSCIOUS project focusses on context-aware anomaly detection in industrial machines and processes. In these complex environments, anomaly detection remains a major challenge caused by the highly dynamic conditions in which these assets operate. The overall objective is to research effective solutions to achieve a more accurate, robust, timely and interpretable anomaly detection in complex, heterogenous data from industrial assets by accounting for confounding contextual factors.
• The TRACY project aims to investigate how to optimally use the log data generated by industrial assets and refine existing AI and machine learning techniques targeted at time series analysis. To this end, TRACY will research how to handle the complexities of log data, e.g. the heterogeneity of the industrial assets, the lack of standardisation amongst log data and the scalable interactive visualisation of the heterogeneous data. 

Producing green energy is an expensive process. In the eight offshore parks off the Belgian coast, there are about 400 wind turbines that supply energy to 2.2 million families. Keeping this infrastructure operational is not only extremely expensive, but also crucial for a stable power supply. The modern wind turbines are therefore equipped with about 800 sensors that transmit data every second. The data is interpreted in real-time, on the one hand to monitor the condition of each machine and to be able to plan the necessary maintenance work, and on the other hand also to improve energy production.

The research within the Flanders AI Research Program

Thanks to AI, wind turbines can inform the database and each other to adapt to real weather conditions. In this way, AI can ensure that the wind turbines are not overloaded and that production is more efficient.

Connect

• Contact persons:  Jan Helsen (VUB AVRG), Ann Nowé (VUB AI Lab)
• Research groups: VUB Acoustics & Vibrations Research Group, VUB Artificial Intelligence Lab

More info and related projects

• The SuperSized 4.0 project aims to create an integrated methodology for wind turbines capable of assessing the risks for long-term underperformance, downtime due to failure and excessive lifetime consumption.
• The H2020 EU-funded PLATOON project provides new approaches and analytics tools for Energy Big Data, thus supporting the zero-carbon transition and developing new services in the energy domain. In an increasingly complex and heterogeneous environment, PLATOON enables the evolution from a classical centralised energy sector to a more distributed one, with intermittent renewable energy sources and new extended digital capabilities. While contributing to artificial intelligence, interoperability, data privacy and security, PLATOON adheres to the standards of the International Data Spaces Association (IDSA), aiming thus to realise the first IDS-compliant Data Marketplace for the Energy sector.
• The MaSiWEC project aims to create a synergetic framework for “Material Based Probability of Failure (MB-PoF)”. This framework is validated and strengthened by an on-shore turbine measurement campaign executed by NREL and a measurement campaign in a Flemish off-shore wind-mill park.
• The Rainbow project will provide the offshore wind energy industry with an improved understanding of the drivers for leading edge erosion (LEE) of the offshore wind turbine blades, resulting in new predictive maintenance capabilities, and optimised strategies for inspection, maintenance & repair.
• Position paper on state-of-the-art technologies for wind turbine drivetrains in collaboration with multiple universities of the European Academy for Wind Energy (EAWE).
• Article on Digitale Toekomst

The Low Voltage (LV) distribution grid (the part of the electric grid to which the end consumers and prosumers are connected) is a critical infrastructure for our society and an important enabler for the energy transition in Flanders.  Hence, we have to make sure that the LV grid does not jeopardize the transition to a sustainable energy future, due to limitations when dealing with a significant increase in electricity demand.  The use of our distribution networks is changing rapidly due to, among other things, the growth of renewable energy production (solar panels) and the electrification of transport and heat sources (charging of electric and hybrid cars and heat pumps). In Flanders, the charging of electric cars and the use of household appliances for additional frequency reserves are a particular concern given that they lead to higher loads on the grid, an increased synchronicity, which in turn leads to higher peak loads.

The LV grid as we know it today, was built over the course of the last century using a “fit and forget” strategy: install (over dimensioned) cables with sufficient capacity to cover all demand peaks. However, with the massive rollout of technology linked to the energy transition, the required capacity when using present safety margins would lead to a very high investment cost. A business-as-usual approach can hence lead to the LV distribution grid becoming a financial and practical obstacle to the transition to a sustainable energy system. The alternative is the development of technology to use the installed capacity more optimally by operating our grids closer to their limit, and to technically support measures to mitigate the impact of the energy transition.  Although the workings of electric grids are well understood, the specific challenge with the LV distribution grid is the limited, incomplete, or incorrect available data: the layout and electric properties of the grids are only partially known, and measurements are very limited and rarely real time. Also, the scale of the problem is vast: Flanders counts with 40k LV transformers, 240k LV feeders, and 3.5M connection points. By combining newly available grid data, e.g., from digital meters, with AI techniques, we aim to calculate the grid load in detail both in time and space to assist distribution grid operators in their infrastructural and operational decisions to make more optimal use of the existing grid capacity and to avoid massive investments in new cables.

Results

Members of our team obtained the third position in the IEEE-CIS Competition titled “Technical Challenge on energy prediction from smart meter data” with a ratio-based approach. The proposed method consisted of different steps applied sequentially: pre-processing, data augmentation and normalization, clustering, prediction based on ratios and finally ensemble learning and post processing. In another work, we introduced a new two-layer hybrid Neural Network architecture called “Decomposition-Residuals Deep Neural Network (DR-DNN)” that was successfully used for day-ahead electricity demand forecasting. This architecture consists of one decomposition layer in which the raw time series are decomposed into trend, seasonal and residual signals, and one Deep Neural Network (DNN) layer for modelling unknown nonlinear patterns.

We have developed and implemented an interactive ILP (Inductive Logic Programming) method for learning relational concepts, such as house-cable connections, from examples. We are currently working towards the application of this method to the GIS (Geographic Information System) dataset provided by Fluvius (the Flemish distribution grid operator), to better determine the house-cable connections of a city in Flanders.

We have clustered consumption user profiles using an active semi-supervised method, COBRAS.  The method incorporated a new dissimilarity measure proposed exclusively for this use case, as well as a new time series representation for flexible supervision expert feedback. Finally, by applying K-medoids clustering on voltage time series from smart metering, we were able to validate grid topology data and derived which household is connected to what electrical phase

More information

• Contact person: Koen Vanthournout (VITO/Energyville)
•  Research groups:  KU Leuven – STADIUS, KU Leuven – DTAI and VITO/Energyville