Up to speed in six months
This might at first sound simple but in fact entailed six months of detailed work, since almost no two plants are the same and the operating conditions vary from one location to another. A great deal depends on differences in climate and on customer requirements. Thanks to the massive data stream and large number of facilities in operation worldwide, the engineers can now see which parameters indicate an imminent system failure and which represent the optimum condition.
“We used mathematical models to analyse the historical data and predict what might be a healthy value for the relevant sensors on a future date,” describes Markus Frondorf, Founder and CEO of anacision – the data science company supporting Linde with this project. During the pilot phase, which ran until the end of 2017, the team of engineers and data analysts compared the desirable healthy value from their calculations with the corresponding measurement from the historical data set. “What we want is an early warning system to alert us to all impending failures. So, our model searches through the historical trove of data to predict possible outages.”
The predictive power of algorithms
More than a year after the project was kicked off, the algorithm is ready for operational deployment. Checks against the historical data reveal that it would have flagged up many failures weeks in advance if it had been running at the time. Forstner is confident: “We have developed predictive analytical capabilities with this algorithm.”
The remaining question is how effectively Linde will be able to use this new algorithm. Not that the company lacks experience in maintaining its own facilities, but the efficiency gains of decisions based on the algorithm will only emerge in 2018, when it transitions from the lab to a live environment in Asia.
Thomas Heinzerling, Head of Regional Operations, is confident that the algorithm will help to turn unplanned downtime into scheduled maintenance windows – and, above all, pave the way for more dynamic, demand-driven maintenance schedules. “Sometimes we look inside a compressor after six years and find nothing wrong.” In these cases, the software could indicate that maintenance should be pushed out. The opposite also holds true: maintenance could be pulled forward if the parameters signal a downward curve.
In the long term, Heinzerling anticipates that the predictions and analyses delivered by algorithms will become increasingly detailed – to the point where the installation team will know what to expect even before they look inside the system. Ultimately, Linde might also be able to pinpoint equipment offering scope for structural optimisation and these insights could be channelled into operational or even engineering enhancements.
This article was first published in the 2017 Linde Annual Report. Download a pdf of the full report here.