Monitoring complex systems is essential for preventing failures and ensuring operational safety. This paper proposes a method that combines ontology which provide structured knowledge representation with stream reasoning to enhance anomaly detection in dynamic environments. Unlike monitoring systems, our approach focuses on interpretable and scalable analysis of continuous data streams, enabling systematic identification of deviations from expected behavior. We demonstrate the applicability of this framework in monitoring lithium-ion batteries, where early detection of thermal anomalies is critical. By integrating a knowledge-driven model with data stream analysis, our method improves the reliability and safety of complex systems while offering explainable insights into detected anomalies.

Quality assurance in manufacturing companies is an essential process for ensuring that products meet established standards. It contributes to customer satisfaction, as well as the reduction of the costs associated with defects. With Quality 4.0, an extension of Industry 4.0 to quality assurance, new possibilities in terms of product quality management are emerging. Thanks to expert knowledge, data collected by machine sensors can be used to anticipate quality issues or manufacturing errors. To semantically detect those situations, an ontology representing manufacturing knowledge linked to quality detection situations is needed. Moreover, as heterogeneous data streams have to be integrated, a combination of stream processing and of-line reasoning can be used. This combination allows a continuous process of data and the use of expert knowledge to detect anomalies. This paper presents an approach for detecting manufacturing quality losses. Therefore, an ontology-based context for manufacturing is introduced to detect quality issues situations. Then, an extension of an existing model using stream reasoning to process heterogeneous data from sensors and predictions is presented to detect the situations continuously.

In today’s energy-dependent world, ensuring the safety and efficiency of lithium-ion batteries is crucial. Early representation of anomalies becomes essential for optimizing performance, reducing disruptions, and prolonging battery lifetime in electric vehicle applications. This objective necessitates the integration of data from distributed and heterogeneous sources, a challenge traditionally tackled by semantic web technologies. In response, this paper introduces an ontology-based model that capitalizes on representing anomalies in lithium-ion batteries. Ontologies play a vital role in representing knowledge in a machine-interpretable format. Our approach enriches sensor data with contextual information, employing structured concepts, rules, and semantics specifically designed for representing anomalies in lithium-ion batteries.

International audience

A critical aspect of managing lithium-ion battery packs in electric vehicle applications is accurately determining the State of Charge (SoC). There are several methods available to estimate it, including coulomb counting with direct evaluation, Open circuit voltage, kalman filter with adaptive approach, particle flter, as well as fuzzy logic and data-based approach. In this paper, we use the state of charge data already computed by a data-driven approach and combine it with an ontology of a battery pack. The built ontology models the battery pack, taking into account the topology, types of cells and their organization inside. To make an exact estimation, different strategies of balance control of the cells are considered. SWRL rules are used to compute the state of charge of the whole battery pack. Matlab Simulink multi-physics model of a lithium-ion battery is used to provide simulated data for the experiments. The given model is evaluated based on regression metrics showing its performance.

The development of Internet of Things (IoT) creates large amount of data usable by decision making systems in various domains. In particular, in the field of health monitoring, it enables to follow the medical state of a patient at home in real-time. A challenge is to interpret these data with a high-level representation model in order to have a better understanding of the medical state of a patient. We propose in this article to use Stream Reasoning associated to an ontological representation of the medical context of a patient to understand her situation. This permits to combine in real time static knowledge stored in an ontology and dynamic information provided by smart sensors. To facilitate this process, we introduce constraints and situations concepts to ease the translation of expert knowledge into logical queries. We provide in this paper an experimental analysis of real body temperature data to illustrate how situations may be detected.

Cognitive Vision Systems have gained a lot of interest from industry and academia recently, due to their potential to revolutionize human life as they are designed to work under complex scenes, adapting to a range of unforeseen situations, changing accordingly to new scenarios and exhibiting prospective behavior. The combination of these properties aims to mimic the human capabilities and create more intelligent and efficient environments. Contextual information plays an important role when the objective is to reason such as humans do, as it can make the difference between achieving a weak, generalized set of outputs and a clear, target and confident understanding of a given situation. Nevertheless, dealing with contextual information still remains a challenge in cognitive systems applications due to the complexity of reasoning about it in real time in a flexible but yet efficient way. In this paper, we enrich a cognitive system with contextual information coming from different sensors and propose the use of stream reasoning to integrate/process all these data in real time, and provide a better understanding of the situation in analysis, therefore improving decision-making. The proposed approach has been applied to a Cognitive Vision System for Hazard Control (CVP-HC) which is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) and has been designed to ensure that workers remain safe and compliant with Health and Safety policy for use of Personal Protective Equipment (PPE).

With the coming era of Industry 4.0, more assets and machines in plants are equipped with sensors which collect big amount of data for effective on-line equipment condition monitoring. Monitoring equipment conditions can not only reduce unplanned downtime by early detection of relevant situations like anomalies but also avoid unnecessary routine maintenance. For the detection of these situations it is necessary to integrate distributed, heterogeneous data sources and data streams. In this context, semantic web technologies are increasingly considered as key technologies to improve data integration. However, they are mainly used for data that is assumed not to change very often in time. In order to tackle this issue, stream reasoning combines reasoning and stream processing methods. Such a combination enables the processing of dynamic and heterogeneous data continuously produced from a large number of sources and implementing real-time services. This paper presents an approach that uses stream reasoning to identify in real time certain situations that lead to potential failures. Early detection enables to choose the most appropriate decision to avoid the interruption of manufacturing processes. In order to achieve this, data collected from sensors are enriched with contextual information. The use of stream reasoning allows the integration of data from different data sources, with different underlying meanings, different temporal resolutions as well as the processing of these data in real time.

Following the trend of Industry 4.0, automation in different manufacturing processes has triggered the use of intelligent condition monitoring systems, which are crucial for improving productivity and availability of production systems. To develop such an intelligent system, semantic technologies are of paramount importance. This paper introduces an ontology that will be used to develop an intelligent condition monitoring system. The proposed ontology formalizes domain knowledge related to condition monitoring tasks of manufacturing processes. After introducing the ontology in detail, we evaluate the proposed ontology by instantiating it with a case study: a conditional maintenance task of bearings in rotating machinery.

Industry 4.0 is an initiative combining a set of technologies that help to achieve more efficient manufacturing processes. An important characteristic for industrial production in Industry 4.0 is that physical items such as sensors, devices and enterprise assets are connected to each other and to the Internet. In this environment, devices and sensors generate increasing amount of data. A key point to consider is that the execution of industrial processes should depend not only on their internal state and on user interactions but also on the context of their execution, in order to become context-aware and provide added-value information to improve the monitoring of operations and their performance. Ontologies emerge as a relevant method for representing manufacturing knowledge in a machine-interpretable way. Therefore, an ontology-based context model for industry is introduced in this paper. The model facilitates context representation and reasoning by providing structures for context-related concepts, rules and their semantics.