5G-RedCap Optimization
This product describes how to incorporate a throughput factor in existing Outer Loop Link Adaptations for a 35% better throughput, reduced transmission failures and lower control message overhead.
- Enhances network performance for IIoT devices, which boosts efficiency for industrial maintenance operations.
- Enables real-time data collection and analysis, supporting immediate maintenance actions and reducing equipment downtime.
- Product will support increasingly complex, future IIoT ecosystems.
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Anomaly Detection Model Selection for Recoater Streaking
Offers a selection of procedures for organizations that require an anomaly detection method for recoater streaking and suggests appropriate anomaly detection methods based on the organization’s needs.
- Provides immediate usability for maintenance teams to detect and mitigate manufacturing defects.
- Promises a reduction of defect rates, improving product quality.
- As additive manufacturing continues to grow in industrial applications, organizations will increasingly rely anomaly detection methods.
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Automated Fault Tree Inference Methodology
Automatically generate fault tree models from failure data using multi-objective evolutionary algorithms.
- Automates the complex and time-consuming task of fault tree construction, saving time and resources for organizations and making reliability analysis more cost-effective and scalable.
- Allows organizations to automate fault tree generation from failure data.
- As production equipment increases in complexity, tools for reliability analysis need to be increasingly automated/autonomous to support maintenance staff, this process has just begun so this product remains relevant.
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Bearing Test System
Physical set-up to generate failure data for bearings, facilitating better understanding and validation of predictive algorithms.
- Generates run-to-failure data of bearings under configurable operational circumstances.
- The modular bearing test rig is open to input from external parties for generating custom datasets.
- As PdM matures and sensor technologies evolve, the demand increases for controlled, reproducible failure data and an independent platform to evaluate emerging PdM solutions.
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Condition-based Maintenance Transition Guide
A comprehensive set of guidelines that supports organizations’ transition from traditional to condition-based maintenance procedures.
- Valuable for organizations that want to adopt condition-based maintenance procedures but are struggling to do so successfully.
- Tools are based on real-life maintenance operations from organizations.
- With the adoption of condition-based maintenance in industry lagging academic advancements, increasing adoption will remain a relevant issue over time.
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Data Selection Criteria for PdM Applications
Data selection criteria that specify which parameters have the highest impact on the failure prediction of assets, which has been tested on generic impeller pumps.
- Tested on generic impeller pumps for optimizing pump maintenance schedules.
- Can provide value to organizations by preventing investments in non-relevant data types (sensors, collection), thus reducing the implementation costs, while increasing efficiency of maintenance operations.
- Long-term relevance in industries reliant on fluid machinery like water treatment and oil & gas.
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Degradation Modeling Methodology for Sewage Infrastructure
Methodology that employs discrete-time Markov chains to model degradation in sewage infrastructure systems, focusing on selecting appropriate models (homogeneous vs. inhomogeneous) for accurate predictions.
- Enhances the ability of infrastructure managers to predict degradation and optimize maintenance schedules through selection appropriate models.
- Successfully applied in a large-scale case study of sewage infrastructure in the Netherlands.
- PdM technology in this field is mature, but the adoption is still low, so sewage infrastructure organizations can benefit from improving capabilities of failure prediction tools.
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Enhanced Reliability Analytics
A comprehensive collection of reliability analysis methods, with computation and verification techniques.
- Versatile toolkit for organizations to assess system reliability, allowing them to select the most suitable method for their needs.
- Practical examples through case studies.
- As reliability is increasingly important with complex assets, methods that promote reliability will remain valuable.
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Human Factors Framework for Maintenance
Framework that provides an approach to studying human factors in a maintenance environment by conceptualizing how changes to an employee’s working context affect their job, behavior, and subsequent performance.
- Comprehensive tool to analyze the impact of PdM implementation on maintenance employees.
- Tested in three case studies and on theory.
- Organizations that struggle with implementing PdM due to employee resistance may use this framework to understand this resistance.
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HVAC Malfunction Detection Framework
Machine learning framework designed for detecting malfunctions in train-based HVAC systems.
- Immediate use in railway systems for maintenance scheduling.
- Product is scalable and uses a structure that allows for easy extension to other models and machinery.
- Long-term relevance as IoT and AI are increasingly integrated into infrastructure maintenance.
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Impact Identification Tool
A systematic methodology and algorithmic framework for identifying and categorizing impacts on aerospace composite structures using piezoelectric and optical sensors, validated through finite element modeling and sensitivity analysis.
- Impact force reconstruction allows for more efficient inspection strategies, as (now) mandatory inspections can be skipped for low severity impacts, thus saving time and reducing costs.
- Development of use of optical fibres for impact identification / force reconstruction.
- Application field can be extended from aerospace applications to marine applications (currently being undertaken).
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Maintenance 4.0 Readiness Assessment Methodology
A methodology to help organizations assess and prepare their infrastructure for Industry 4.0 technologies, particularly focusing on sensor and network adaptation for maintenance tasks.
- Evaluate organization’s readiness for digital transformation in maintenance, offering consultancy opportunities.
- Supports strategic planning for maintenance departments, enabling them to upgrade systems systematically.
- Enables transition from traditional to advanced maintenance procedures.
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Maintenance Planning Toolkit
Toolkit that leverages deep reinforcement learning (DRL), graph convolutional networks and Markov decision processes to optimize maintenance planning for infrastructure systems.
- Reduces maintenance costs and downtime for infrastructure organizations by optimally grouping maintenance actions.
- Tested on practical use cases, so easily applicable to new settings.
- Infrastructure in NL is aging, so new infrastructure should be installed with state-of-the-art methods, which this product provides.
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Predictive Maintenance Framework
Predictive maintenance implementation framework with user manual.
- Implementation of a predictive maintenance framework that can be used by the companies to further develop their own predictive maintenance tool.
- Showcases all PrimaVera algorithms developed and provides a manual on how to implement them.
- Contains two working demonstrators.
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PrimaVera Product Catalogue
The PrimaVera Product Catalogue synthetises and displays the outcomes of the PrimaVera project as products. The products, if fully developed, can be a resource to the project’s industrial partners. The characteristics of each product are represented in a taxonomy. Products can be searched and filtered using these characteristics in the online Catalogue.
- Enables both and industry partners to learn what was done in the PrimaVera project, and allows interested parties to contact researchers about specific products.
- Showcases the outputs (around 90) of the research project as a set of knowledge products (around 20) with marketable value, practical applicability, and long-term relevance beyond the project’s lifespan.
- The method for developing a generic Knowledge Product Catalogue (KPC) can be re-used in future research, development, and innovation projects.
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Prognostic Approaches for Asset Health
A toolkit for predicting the remaining useful life of assets under varying conditions and failure mechanisms, with a selection procedure to assess the most appropriate procedure for various settings.
- Facilitates asset health predictions/prognostics in a variety of contexts.
- Demonstrated applicability through case studies.
- Pioneering technology, which will only now begin being rolled out, so this product will help organizations for the foreseeable future.
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Railway Digital Twin Development Toolkit
Framework for creating and utilizing digital twins in railway maintenance that provides methodologies for data collection, processing, and visualization.
- Assists in the development of a digital twin solution to enable predictive maintenance.
- Directly applicable, hands-on guide on digital twin development for railway infrastructure.
- Helps railway infrastructure organizations overcome the inherent difficulties associated with digital twin development.
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Robust Control and Verification Toolkit
Toolkit containing algorithms for designing and verifying robust controllers for systems under uncertainty.
- Useful to industries where high uncertainty is already substantially problematic.
- Toolkit components are applicable to a large variety of maintenance problems.
- As autonomy and complexity in systems grow, which will increasingly happen in the future, robust control becomes more essential.
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Safe Reinforcement Learning Toolkit
Toolkit with methods that ensure safety in reinforcement learning (RL) systems by preventing constraint violations during training and deployment.
- Highly valuable for organizations that want to apply ML methods in safety-critical applications.
- Tools provided can be implemented into ML methods immediately.
- Increasing demand for ML methods in safety-critical applications will increase the need for this product.
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SAFEST – Fault Tree Analysis Tool
A fully automatic, scalable, and state-of-the-art risk-assessment tool that is based on dynamic fault trees (DFT) and event trees.
- Can faithfully assess the risk of fail-operational/fault-tolerant dynamic systems with decision-making capabilities.
- Directly usable by reliability engineers and safety analysts to model systems.
- Scalability and increasing demand ensure it remains relevant for analyzing future, more intricate systems.
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Safety-Security Analysis for Critical Infrastructure
A risk analysis framework integrating fault trees and attack trees to evaluate vulnerabilities in critical infrastructure.
- Emerging marketability, as cybersecurity in industrial systems gains traction.
- Can be used for scenario-based risk assessments.
- Long-term relevance as cybersecurity threats are increasingly numerous and severe.
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Spare Parts Inventory Management Toolkit
Toolkit containing solutions for setting up an organization’s spare part policy.
- Enables organizations to utilize optimal spare parts levels based on historic spare parts consumption, which may reduce holding costs for organizations.
- Solutions provided can be implemented immediately to achieve savings in spare parts inventory management.
- As costs for high-tech spare parts increase, (near-)optimal spare parts policies are increasingly important for cost-effective maintenance operations.
