Programming skills
Cloud computing
Mobile app development
Programming skills
DevOps engineering practices
To this day, I am pursuing my doctoral degree in Electronics and ICT Engineering Technology at the PreDiCT research team of IDLab, University of Ghent. My expertise and specialization includes machine learning, recommender systems, cloud computing and dynamic dashboards.
Master of Science in Electronics and ICT Engineering Technology - Graduated Magna Cum Laude
Bachelor of Science in Electronics and ICT Engineering Technology
Artificial Intelligence for IT Operations (AIOps) addresses the rising complexity of cloud computing and Internet of Things by assisting DevOps engineers to monitor and maintain applications. Machine Learning is an essential part of AIOps, enabling it to perform Anomaly Detection and Root Cause Analysis. These techniques are often executed in centralized components, however, which requires transferring vast amounts of data to a central location. This increase in network traffic causes strain on the network and results in higher latency. This paper leverages edge computing to address this issue by deploying ML models closer to the monitored services, reducing the network overhead. This paper investigates two architectural approaches: a sidecar architecture and a federated architecture, and highlights their advantages and shortcomings in different scenarios. Taking this into account, it proposes a framework that orchestrates the deployment and management of distributed edge ML models. Additionally, the paper introduces a Python library to assist data scientists during the development of AIOps techniques and concludes with a thorough evaluation of the resulting framework towards resource consumption and scalability. The results indicate up to 98.3% reduction in network usage depending on the configuration used while maintaining a minimal increase in resource usage at the edge.
Manufacturers can plan predictive maintenance by remotely monitoring their assets. However, to extract the necessary insights from monitoring data, they often lack sufficiently large datasets that are labeled by human experts. We suggest combining knowledge-driven and unsupervised data-driven approaches to tackle this issue. Additionally, we present a dynamic dashboard that automatically visualizes detected events using semantic reasoning, assisting experts in the revision and correction of event labels. Captured label corrections are immediately fed back to the adaptive event detectors, improving their performance. To the best of our knowledge, we are the first to demonstrate the synergy of knowledge-driven detectors, data-driven detectors and automatic dashboards capturing feedback. This synergy allows a transition from detecting only unlabeled events, such as anomalies, at the start to detecting labeled events, such as faults, with meaningful descriptions. We demonstrate all work using a ventilation unit monitoring use case. This approach enables manufacturers to collect labeled data for refining event classification techniques with reduced human labeling effort.
In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to be updated in terms of displayed sensors. These requirements motivate the development of dynamic dashboarding applications. These, as opposed to fixed-structure dashboard applications, allow users to create visualizations at will and do not have hard-coded sensor bindings. The state-of-the-art in dynamic dashboarding does not cope well with the frequent additions and removals of sensors that must be monitored—these changes must still be configured in the implementation or at runtime by a user. Also, the user is presented with an overload of sensors, aggregations and visualizations to select from, which may sometimes even lead to the creation of dashboard widgets that do not make sense. In this paper, we present a dynamic dashboard that overcomes these problems. Sensors, visualizations and aggregations can be discovered automatically, since they are provided as RESTful Web Things on a Web Thing Model compliant gateway. The gateway also provides semantic annotations of the Web Things, describing what their abilities are. A semantic reasoner can derive visualization suggestions, given the Thing annotations, logic rules and a custom dashboard ontology. The resulting dashboarding application automatically presents the available sensors, visualizations and aggregations that can be used, without requiring sensor configuration, and assists the user in building dashboards that make sense. This way, the user can concentrate on interpreting the sensor data and detecting and solving operational problems early.
The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies.
Those who earn the Google Project Management Certificate have completed six courses, developed by Google, that include hands-on, practice-based assessments and are designed to prepare them for introductory-level roles in Project Management. They are competent in initiating, planning and running both traditional and agile projects.