Howden has been a part of that mix early on and now has an update to one of its flagship IIoT offerings – the digital twin, part of Uptime. The company’s latest digital twin features upgraded analytics designed to provide a clearer and complete picture of an operator’s machinery continuously and instantaneously.

　　“The most recent iteration has the specificity to integrate more actual operating data relative to the compressor’s environment and the duty it is supposed to perform,” said Mirko Melisie, Howden’s lead data-driven advantage associate. “We have added riderband wear trending and valve leakage trending into the portal.”

　　The new digital twin can obtain more information than past iterations due to its upgraded model-driven and data-driven analytics. The model-driven analytics are based on physics and simulation-based data models and the data-driven variation uses enhanced algorithms to identify and predict problems.

　　“Many digital twins are only based on data-driven analytics using specific algorithms for supervised or unsupervised machine learning,” Melisie said. “The Howden digital twin also uses the physics-based model that we use to design new compressors. The benefit of this approach is that we can see whether an asset is running as it should or not directly after installing the Howden Uptime system.”