Components

Fatigue Monitoring System (FaMS)

 

Module type(s)
Reasoning Engine
Description
FMS uses an artificial intelligence (AI) model relying on machine learning which estimates exertion level of subjects based on static data (e.g. age, weight, etc.) and dynamic data (e.g. HR, EDA, skin temperature).
Adopted technologies and
methods

Apache Spark ML framework; Scala; Apache Avro; Docker; Random Forest Classifier.
Input
Workers physiological data from wearable devices on workers (e.g. HR, HRR, etc.); static data collected through questionnaire (e.g. age, needs, expectations, experience, etc.).
OutputWorker fatigue level to be used by “decision maker” modules to make human aware decisions.
FeaturesAdoption of machine learning to create use case specific AI model; Calculation in real-time of exertion stress level; Dynamic features use; Multi-device compatibility.

Shop-Floor Anomaly Detection System (SADS)

 

Module type(s)
Decision Maker
DescriptionSADS monitors the real-time status of the production assets and uses artificial intelligence (AI) to detect anomalies with regards to sensor values or behaviour.
Adopted technologies and
methods
Docker; Python; scikit-learn and other libraries; TensorFlow.
InputAny data from the shop floor, including sensor data, diagnostic messages, audio and video data streams.
OutputAnomaly score and health index per asset used by the ‘decision maker’ to derive recommendations (e.g. change configuration, conduct maintenance, emergency shut down).
FeaturesAdoption of machine learning to create use case specific AI model; Calculation of asset health in real-time; Dynamic features use; Multi-device compatibility;

VIsion for Quality Excellence (VIQE)

Module type(s)Sensing; Reasoning Engine
DescriptionVIQE uses an artificial intelligence (AI) model based on deep learning for defects detection and Convolutional Neural Network for quality detection.
Adopted technologies and
methods
OpenCV; MVTech Halcon; VIDI; TensorFlow; C#.
InputImages from cameras; 3D sensors and 2D sensors data.
OutputQuality control values and results
FeaturesUse of computer vision algorithms and Deep Learning for Quality Inspection.

AI for Quality Systems (AIQuS)

Module type(s)Reasoning Engine
DescriptionAIQuS applies AI approaches for retaining knowledge (Continuously Learning Systems and Continual AI techniques), to facilitate the setting-up and the operation of the quality inspection systems. AIQuS uses extracted patterns and expert knowledge to infer efficient systems’ configurations and alerts in case of misconfigurations. Moreover, based on a set of annotated samples, AIQuS guides the operators in systems’ future adjustments, simplifying the tasks and reducing the potential errors.
Adopted technologies and
methods
Python+Keras (Tensorflow); Reinforcement Learning, using Gym (OpenAI); Deepmind; ReAgent; Ontologies and Semantic Web best practices.
InputProduct measures and tolerances; defect categorisation; experts’ knowledge.
OutputParameters for the quality control systems to be used by workers for systems reconfiguration.
FeaturesSemantic Analysis to structure the data: Collection of Samples; Annotation; Classification; Context Mapping to acquire contextual information.

Intervention Manager (IM)

Module type(s)Decision maker
DescriptionIM monitors the real-time status of the worker-factory ecosystem, knows what interventions can offer and is capable to assess the available ones and decide which is the best one.
Adopted technologies and
methods
JAVA 8; Spring Boot; Vaadin; OpenFeign; JUnit4; Apache Avro; Docker.
InputAny data from worker-factory ecosystem.
OutputInterventions (e.g. system configuration, machine parameter, task assignment, etc.)
FeaturesSelect interventions based on the inputs of the Sensing and Reasoning (AI based) modules; trigger the selected intervention; extensible in terms of new interventions, new conditions, new tuples; extensible by adding or removing devices and/or technologies.

Manufacturing Process Management System (MPMS)

Module type(s)Decision maker
DescriptionMPMS is used to design in BPMN and execute a manufacturing process, along with alternatives and exceptions. The process includes high-level tasks that are designed to be executed by one actor (e.g. robot, human worker etc.) or many of actors.
Adopted technologies and
methods
Java; REST API; BPMN; XML; JavaScript; HTML.
InputProcesses; Exceptions; Resources (actors); Data from sensors; Human inputs & decisions.
OutputTask lists to actors; current status of process and actors.
FeaturesDefine resource allocation and task assignment; monitor process status (e.g. times, utilisation, task heatmap etc.); support to process design and modelling.
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