Inspect, measure, monitor and predict
Providing an insight into assets is the core of Asset.Insight. Being able to accessibly review assets enables asset owners and asset managers to perform maintenance in the most efficient and effective way possible. This ensures greater safety for uses and saves time and money. Asset.Insight. gathers data through inspections, measurements, and continuous monitoring. Internet of Things (IoT) and machine learning algorithms make it possible to translate date into clear and poignant insight using “predictive modelling”.
"Lightning-fast technological development and ever-changing client requirements – the world around us is rapidly changing. In order to be able to address these changes, we have to continually keep innovating. Asset.Insight. offers added value by combining asset expertise with innovative data analytics techniques" says Paul de Hair, general director for Asset.Insight. We would like to share our expertise with you. Fully understanding your optimisation issue is the foundation for our work.
Asset digitisation and predictive modelling
Data can be gathered using measurements or inspections. Asset Insight is able to digitise assets using sensors on a measuring train or measuring car, a 3D scanner that can be mounted on boats or cars, or using a drone. Asset.Insight. uses predictive modelling to offer its clients a timely insight into the steps they need to take with respect to asset management in order to safeguard their users’ safety.
Predictive modelling of rolling contact fatigue of rails
One example is the development of a model for rolling contact fatigue (RCF) of rails. Fatigue is caused by initiation and growth of cracks. The burden of passing trains and local conditions cause wear on tracks and result in damage to the rails. These rails occasionally need replacing. A timely insight into the location of onset damages makes preventative maintenance a possibility. The Eddy Current Lorrie, an innovative measurement instrument, can detect onset tears and cracks with a depth between 0.5 and 5 millimetres in the rail. Deeper tears can be measured using ultrasonic technology, or soundwaves. The predictive model enables the implementation of preventative maintenance. This improves the economic lifespan of rail bars at low cost.