We are proud to announce that the XAIPre, eXplainable AI for Predictive maintenance, project is awarded by NWO in the Smart Industry 2020 program. Leiden University together with Heerema Marine Contractors and Hanze Hogeschool will develop and research predictive maintenance techniques with applications in the maritime industry.

The goal of this project is to create predictive maintenance technology that is on one hand transparent and easy to use for engineers, and on the other hand can offer detailed insight into the current state of the machinery at hand. Transparency is key here. Many predictive maintenance methods make use of “black-box” machine learning, meaning that what happens between the input data and the output data is unbeknownst to the user. In turn, adoption of these tools can be a frustrating process.

The XAIPre project (pronounce “Xyper”) aims at developing Explainable Predictive Maintenance (XPdM) algorithms that do not only provide the engineers with a prediction but in addition, with 1) a risk analysis should the maintenance be delayed 2) the criteria or indicators used to make that analysis.  By providing more insight into the state of the machine, the engineers are empowered and given control over their maintenance process.

In practice, this might look like this: an engineer would usually maintain the equipment on an offshore location once a week, depending on vessel schedules. Should a vessel schedule suddenly change, the engineer can use the technology to assess the equipment’s current condition. The sensors in the machinery can provide data about key indicators, such as heat or friction, to the algorithm. The algorithm then provides the engineer with a risk analysis (e.g. maintenance is required before the vessel is scheduled to depart) and the key indicators that influence this analysis (e.g. a component is heating up faster than is ideal).