In manufacturing, reliable healthy equipment is a fundamental requirement. Yet so many of us struggle with unplanned downtime due to equipment failures.

Digital transformation often has as its first arena in manufacturing machine health.

Initial efforts at machine health have revolved around preventive maintenance executed on the schedule recommended by the manufacturer of the equipment. Some of us are better at that than others.

Another step is knowing if equipment is running at the speed it was designed for. Frequently we run it at a slower speed to optimize how it works for us. That’s an indication of something wrong.

But all of that is merely addressing the basics.

The machine health part of a digital transformation — that is using data and analytics to understand the “why” of our machine operations — takes us to a much better place.

Goals include zero unplanned downtime, cost-effective maintenance, and awareness of the operating environment that best supports the health of the equipment. Predictive maintenance is part of that, but not all of that.

Knowing all critical influences on machine health allows your operations to lengthen the life of the equipment, produce higher quality output faster, and reduce costs.

Do you wish you knew for certain how ambient temperature and humidity, fluid age and viscosity, vibration, changeovers, variations in power to the equipment and more impact the health of each piece of your equipment? With data and data analytics, you can do that.

Every manufacturer has different types of equipment of different ages made by different manufacturers. Each of those pieces of equipment has a different history of use, maintenance, and handling. With data, you can understand all of it, regardless of those distinctions.

Industry 4.0 and the digital component of it are instrumental in the future of every manufacturing company. Machine health is one place to start as you pursue this journey.

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