Five steps to implementing predictive maintenance
Author : By Dr. Simon Kampa, CEO of Senseye
14 January 2019
While the benefits of implementing predictive maintenance are huge, the process for introducing these practices can often appear daunting. In reality, however, it is easier than you might think. Here are five easy steps that any factory can take to ensure they are taking full advantage of Industry 4.0 to boost maintenance practices.
Industry 4.0 and the rise of the smart factory are changing the face of the manufacturing industry at an incredible rate. Gathering of data en masse and the interconnection of machines via the Industrial Internet of Things have created a wealth of opportunities for factories to become more efficient, effective and productive.
Senseye’s expertise, condition monitoring and the predictive maintenance approaches it enables, is an increasingly accessible way for factories to reduce machine downtime and increase the overall efficiency and performance of their production environment. Smart algorithms crunch through data collected from machine sensors to analyse, diagnose and predict failures up to several months in advance.
Here are five easy steps that any factory can take to ensure they are taking full advantage of Industry 4.0 to boost maintenance practices.
1. Use the data your machines produce already
One of the first challenges associated with implementing a predictive maintenance approach is choosing which data to gather, analyse and act upon. We recommend that our clients take a pragmatic approach and analyse the data that their machines already produce.
It is possible to collect and analyse a range of key data sets from most industrial machines and many manufacturers are already gathering it for incident logging or historical analysis. Data relating to machine current, torque or pressure can be all that is needed to spot early signs of problems and a wide range of different failure modes.
2. Start standard, then let the algorithms improve themselves
A key factor in scaling predictive maintenance is to ensure that the algorithms you start with can be applied to any machine from any manufacturer.
Instead of creating bespoke algorithms for each machine, we recommend starting with a series of generic algorithms and letting them improve themselves over time. Our algorithms start smart and get even smarter by learning on the job, automatically figuring out the most appropriate algorithm for each monitored asset and optimising their own performance as they are fed with more data.
3. Leverage the cloud to analyse at scale
While scaling up predictive maintenance initiatives using traditional approaches involved employing more data scientists or retraining engineers with a completely new set of skills, advances in automation and machine learning mean that is now possible to collect and analyse machine data at an enormous scale. Computers can crunch through data from tens of thousands of machines to automatically spot the signs that a machine is failing.
Large amounts of computer resources are required to facilitate the number crunching this kind of large-scale condition monitoring requires. The cloud provides that power on demand and initiatives proven on a small cluster of machines can be expanded rapidly to many thousands more.
4. Set up insights and alerts to utilise your engineering resource better
Once monitoring of manufacturing machinery is largely automated, it is important that machine health and prognostics are reported correctly. Rather than bombarding engineers with large and complex data sets, it is better that these insights are delivered to relevant members of the organisation in a simple, easily understandable format.
Simple alerts can be set up to provide information about matching failure models and likely remaining useful life of each piece of machinery, allowing predictive maintenance to be performed effectively and efficiently without the need for deep pockets or teams of data scientists.
The insights created by smart algorithms are very empowering. Engineers without any previous data science experience can respond to machine failure events within minutes, and – even more impressively – address predicted problems before they can affect production.
5. Be prepared to re-engineer your maintenance culture
The one thing that predictive maintenance algorithms can’t fix automatically is the change in workplace culture they create. Rather than working to predefined maintenance schedules or responding to machine failures, engineers will need to get used to the idea that their workload will be influenced by a computer.
Manufacturers without data scientists on their payroll will find that they can use sophisticated data science without making any further hires or having to retrain existing personnel. Those with experts already will find that they can automate the mundane aspects of those roles and allow them to focus their efforts on more complex and challenging tasks that require a greater degree of creativity and lateral thinking.
Engineers will have to react to the differences predictive maintenance will bring. It could be as simple as changing how they start the day, sitting down to look at a report from Senseye to discover where they should focus their attention that day.
The tools that make large scale predictive maintenance possible are already delivering a substantial productivity boost to the maintenance department and beyond. By monitoring all their machinery and not just the critical points of failure, manufacturers have also been able to halve levels of unplanned downtime, deliver dramatic improvements in throughput, quality and margin, and achieve reductions in maintenance costs of up to 40 percent.
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