There’s an awful lot of discussion right now about the IoT / Industry 4.0 and the benefits it brings to predictive maintenance among other things (in summary, better quality information, delivered faster between machines). We believe that the most exciting application of the Industrial IoT is for prognostics but it doesn’t do much good to talk about the benefits without ensuring that the foundations of your IoT implementation are correct. We’ve put together three key principles which we think are essential to help ensure that you can take advantage of this emerging field and ultimately help to reduce machine downtime.
Condition monitoring is a great concept and technology – the ability in some cases to gain an almost instantaneous insight into the health of your machinery is an essential tool in an effective predictive maintenance regime. We’ve covered ‘condition monitoring in 10 minutes’ here.
Prognostics is difficult. Forecasting the failure of your machinery takes a great deal of experience and time, commodities that are respectively rare and in short supply!
Uptime is simply the opposite of downtime – specifically unplanned downtime. The thing that ruins throughput and productivity, requiring unscheduled remedial maintenance and loss of production. Uptime is when your machines are correctly performing their intended functions – creating useful output and generating money for your business.
Industry 4.0 and the Internet of Things (or Industrial Internet of Things if you prefer) are so closely related that we may as well use the terms interchangeably. Every day there seems to be some news about the Internet of Things and how it’s going to change everything – connected cars, connected people and even connected washing machines. Yet those connections don’t mean a whole lot without real use cases – yes, it’s now possible for your fridge to talk to your phone but that’s answering a question that nobody is asking, solving a problem which nobody cares about (although I’m prepared to eat my words in 5 years’ time when we are all sharing our fridge stats on whatever is the hottest fridge-based social network).
Prognostics – being able to predict when a machine will stop being able to perform its intended function is a key element of effective predictive maintenance and probably the most exciting thing that the Industrial Internet of Things / Industry 4.0 will enable. We’ve put together a short guide to help you understand this technology as applied to the manufacturing sector:
When talking to people we’ve found that the terms Preventative and Predictive can often mentally overlap; yet there’s such a marked difference in implementation techniques and real-world effects that it’s important to clearly define both before we get into their pros, cons and why Predictive Maintenance is the only future for manufacturers.
Or rather how the Industrial Internet of Things helps Predictive Maintenance (maintaining a machine before it breaks down and causes downtime).
Press release: For immediate release
Southampton, UK, 03/05/2016
Senseye, provider of the PROGNOSYS, cloud-based prognostics and condition monitoring solution to the manufacturing industry, is proud to announce that Steve McEvoy, a leader in machinery health monitoring, prognostics and condition monitoring, formerly with GE Aviation Systems and Condition Monitoring Group, has joined Senseye’s advisory board.
Predicting machine failure sounds great (and it is!). Whilst we always try to minimize any pain in getting set up, it’s important to realize that predicting failure is driven by data and in order to make the predictions useful and reliable, the data from your machinery needs to be of a sufficient quantity and quality (although quantity does have a quality of its own).