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.

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.

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.

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.

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).

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.