Predictive maintenance & condition monitoring


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The end goal is for engineers to


spot emerging problems up to six months ahead of time, identify when machines are most likely to fail and align maintenance accordingly so that business continuity is maintained


This is a much more valuable level of data than


simply scheduling in maintenance because of time that has elapsed, or the hours spent with the asset ‘in-use’. It is a critical first step in moving away from the idea of asset key indicators data as a manual work and seeing it as the fuel for a range of critical operational efficiencies.


Doing business beTTer: The mulTiple reTurns on invesTmenT A key factor in performing this analysis cost- effectively at scale is the use of specialised cloud-based computing applications relying on machine learning, A.I. and mechanical engineering frameworks. At the start, these need to be generic enough to be introduced rapidly at scale and used on any machine from any manufacturer, enabling the automatic assessment of machine condition monitoring to begin immediately. The more data these applications are exposed


to, the more precise they can be. The accuracy of the predictions improves exponentially over time as they discover more about each machine’s unique quirks and characteristics. This is a path of ever-increasing returns. If a manufacturer is already using an older


productive, or preventative maintenance strategy, these applications can help enable or


Instrumentation Monthly October 2019


improve the autonomous maintenance that sees operators rather than technicians engage in basic front-line maintenance tasks. Typically, machine operators lack the historical knowledge that technicians have, and technicians are often reluctant to relinquish control. The introduction of these applications means


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that operators on the front line can understand their machines better than ever before. Businesses can ensure that each operator has the right tools and the right knowledge at the right time to get specific maintenance tasks done. Technicians can then be freed up to make a much larger, strategic contribution to the business. It is also worthy of note that these specialised


applications deliver can not only deliver an automated predictive maintenance approach to reduce unplanned downtime, they can also improve the planning of scheduled downtime of an asset as well, ensuring the asset is only offline when repairs are needed. The return on this investment can be


substantial. Large scale manufacturers can halve their levels of unplanned downtime and cut their maintenance costs by around 40 per cent. These savings enable organisations to recoup their spending on such a system within three months of introducing it. The reduction in unplanned downtime is vital.


The cost of machine downtime remains very high: according to the International Society of Automation, $647 billion is lost globally each year. Even at the level of an individual company, every minute that critical machinery is offline can cost tens of thousands of pounds. However, the real value of this investment is


determined by each individual business looking to invest in such applications. Any maintenance operation that meets the needs of a business is built on understanding what data, information and insight that business needs – and as a result the questions it must ask. These questions have always evolved, from


firstly asking what has already happened to an asset so the business can plan and budget for the following year, to modern demands of how to prevent unplanned downtime, lower costs and expedite repairs. Specialised cloud-based applications delivering automated condition monitoring offer a unique ability to not only answer these questions accurately and quickly, but also to propose a better answer. The use of specialised cloud-based computing


applications relying on machine learning, A.I. and mechanical engineering frameworks are already accelerating the existing benefits of predictive maintenance, from failure prediction and fault diagnosis to the recommendation of relevant maintenance actions. For asset-intensive industries, there remains


a real drive to push for the next level of asset availability and reduced costs and risks. Business will continually look to technology to help provide these advantages. Soon, this may include the likes of 5G. Right now, however, it is time for automation, specialised applications and the cloud to contribute to the accelerated evolution of maintenance.


Senseye www.senseye.io 39


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