CIMPLO

On the 12th of May, 2022, the last User Committee meeting of the CIMPLO project took place at Restaurant Scarlatti. The project partners and users look back on a very successful project, which will be continued in a new NWO funded project: XAIPre.

The term uncertainty has different definitions and taxonomies in different research fields. Here, we briefly introduce uncertainty in three contexts: predictive modelling, optimization problem and scheduling research. Uncertainty in predictions In this region of predictive modelling, uncertainty can be defined as the incompleteness in knowledge (either in information or context) that causes model-based predictions to …

In multi-objective optimization, especially in preference-based multi-objective optimization, the knee points usually play an important role. The knee point is a point for which a small improvement in any objective would lead to a large deterioration in at least one other objective. Due to this feature, it  is  widely  believed  that  knee  points are most …

“Prediction is very difficult, especially if it’s about the future!” Niels Bohr, Nobel laureate Modern condition-based maintenance (CBM) relies heavily on the knowledge of the future state of an asset (prognostics and health management – PHM) [1]. This can reduce the risk of (serious) injury and annoyance, allows for saving resources and facilitates predictive maintenance. …

Our maintenance optimization problem, like many other multi-objective optimization problems, can lead to a large objective space. However, finding a well-distributed set of solutions on the Pareto front requests a large population size and computational effort. Therefore, instead of spreading a limited size of individuals across the entire Pareto front, we want to only focus …

Before you ask yourself, no, we do not intend in this article to quantify how much usefulness you – the reader – still have. That would be Orwellian and morbid in the least. Instead, with this article we would like to briefly outline some of the research we are doing on our topic, remaining useful …

Rotating machinery are essential components in most of today’s manufacturing and production industries including, but not limited to, gas turbine engines of aircrafts, car engines, truck engines, pumps, and bearings, etc. Considering the importance of rotating machinery maintenance for the CIMPLO users, we are developing a benchmark for fault detection and estimation remaining useful lifetime …

The Pareto dominance relation, as the most commonly adopted ranking method, plays an essential role in multi-objective evolutionary algorithms (MOEAs). However, its ability is often severely degraded with the increase of the number of objectives [1]. The major reason for this performance deterioration is that individuals are not likely to be dominated by others. Given …

When we train a machine learning (ML) model on data, we are essentially learning a rule that maps X to y, X being the input data and y being the labels (classes in classification or continuous numbers in regression). There have been numerous ML techniques, spanning from simple decision trees all the way to complex …

The goal of the CIMPLO project is to provide industry with a tool for optimizing maintenance activities. When optimizing the maintenance operations, multiple objectives are required to be optimized, such as the processing time, processing cost and production safety, which leads us to a multi-objective optimization problem (MOP). Due to the complexity of MOPs, the …