Special focus SenSeye’S top five manufacturing predictionS for 2019


his year marks an inflection point in the maturity of Industry 4.0 and the application of real-world predictive

maintenance as companies move from pilots to real deployments – with significant ROI. As a provider of industrial predictive

maintenance analytics to several Fortune 500 companies, it is very much Senseye’s area of expertise. The company uses machine learning to monitor the condition of industrial machinery and spot the often small but significant variations in vibration, pressure, temperature, torque, electrical current and other sources that indicate when a machine will fail up to six months in the future. Accurately predicting the future of an

entire industry such as manufacturing is much more difficult – the variables are many more than you would find on a typical piece of industrial equipment. Senseye does, however, come into contact with hundreds of manufacturers around the world every year, so the company has a good idea about where the industry is heading. Here is Senseye’s top five predictions for

the global manufacturing sector in 2019 Senseye


2019 will be the year in which machine manufacturers recognise the opportunity presented by servitisation. More OEMs will move to selling capacity and uptime rather than simply a production asset, and this change will require more visibility into how machines perform and greater data sharing between the users of those machines and their OEMs. Of all of these trends, Senseye is most excited

about servitisation. It will represent the biggest step-change for the industrial sector since the introduction of Industry 4.0. The software industry has demonstrated that a scalable ‘… As a Service’ model can can be effectively integrated into all levels of modern business. Providing the business function of a machine is really no different though requires far more complex data processing and interpretation.


The cost of smart sensing solutions to connect legacy

machinery and enable Industry 4.0

will continue to decline. Manufacturers want data from older machines but have been forced to bolt together their own systems due to a lack of off-the-shelf products. There will be a land grab from systems vendors that have recognised the opportunities for retrofitting machinery. 2019 will not be the year that these become commoditised, but that point is close.


Automated machine-learning driven predictive maintenance will become mainstream. Predictive maintenance has been used in

regulated industries such as aerospace for years, relying on humans to collect and analyse the data for signs of problems. Advances in this area and the ubiquity of cloud computing, together with the ability to gather machine data mean we can now automate condition monitoring and prognostics at a scale and cost that gives an ROI of less than three months in most cases.


2019 is going to be all about the value-add that it brings; using it to monitor machinery, predict problems

before they impact on production, and optimise the efficiency and throughput of manufacturing environments. Manufacturers will turn increasingly to ‘holistic’ cloud platforms and use the data they contain at a greater scale than ever before. This focus on data provides an opportunity for IT to move beyond problem solving and deliver huge amounts of value to the organisations they serve.


While engineers will make greater use of data, using computer

software and tools to access and interpret it, we will not see them

spending more time at their desks. Engineers will be mobilised to spend more time on the factory floor, armed with rugged mobile devices and a range of industrial apps. These will make jobs such as monitoring machine health incredibly easy, being done by a computer in the cloud and the critical bits of information served directly to engineers on the factory floor. 2019 will see real-world case studies for this start to emerge.

Instrumentation Monthly January 2019


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