Scheduling downtime for operations maintenance, especially if there is nothing notably wrong with the machinery, can be a costly pain in the neck. You have perfectly functioning parts but because they\u2019re a few years old, or that warranty is running out, you need to get them tested.<\/p>\n
But what if you didn\u2019t need to plan for these tests? What if instead, you could use sensors and real-time analytics to make predictions based on the condition of the machines to then schedule maintenance. Wouldn\u2019t life, and well, costs, process functioning, safety and product quality be so much better?!<\/p>\n
Enter the IoT and predictive maintenance! What with the advent of Industry 4.0, 5G and Big Data all well and truly coming to the fore, it is now easy to see the tangible benefits of connecting tools and machinery to the Internet to assess their functionality. Rather than setting downtime for machinery only to find they are all working perfectly fine, why not instead harness the power of data modeling to make projections for needs-based maintenance.<\/p>\n
Rather than thinking, oh this sounds like a fantastic idea, I\u2019m going to start putting sensors on everything and tracking all of this data! Think the other way around; what is in your factory environment that uses downtime and costs the organisation a lot of money when it\u2019s not working or is in for maintenance?<\/p>\n
Through mapping out the working environment, you can implement systems to reduce that downtime.<\/p>\n
You\u2019ll also need to further analyse the tools, as some of them will be hard to predict their failure or maintenance-needs. Breaking down the tools into their consecutive parts, and deciding what possible faults can be easily predicted, will help with the development of the big data model and also help you figure out where the sensors need to go.<\/p>\n
If your tools are taking far too much time to maintain or the downtime is costly, it might be time to think about how you can offset those challenges.<\/p>\n
In the automotive industry, predictive maintenance is used to assess each of the parts so that as they progress through the supply chain, you can assess when they may fail. So rather than sending them along the chain, only for them to break and possibly be sent back, the replacement can occur before it breaks.<\/p>\n
In the rail industry, the ability to assess when functions need repairing in advance can save a lot of costs and delayed services. This would greatly increase the train service efficiencies and disruptions.<\/p>\n
Oil and gas industries often require personnel to physically go to hazardous environments to carry out maintenance and testing on tools and hardware. With the aid of sensors, IoT and predictive maintenance, these trips can be reduced unless absolutely necessary, therefore cutting costs and keeping workers safe.<\/p>\n
Although SCADA also offers real-time analytics in enterprises, there is little to no interoperability between machines and software applications, and it also takes a long time to go through all the local historical data and make predictions. With big data and machine learning, the IIoT is much faster at processing the data that is stored and easily accessed in the cloud. A key factor in predicting tools maintenance is analysying the historical data to figure out where something might go wrong in the factory, but with SCADA\u2019s limitations it\u2019s difficult to foresee.<\/p>\n
Like we previously mentioned, all the parts of the tools need to be assessed to configure these health checks. These variables are then what the sensors on the tools will look for, to send the necessary data to the cloud to be analysed.<\/p>\n
There are multiple steps the data must go through in order for the whole system to operate smoothly. The data goes through field and cloud gateways to allow for filtering and processing the data and ensuring correct connectivity.<\/p>\n
A data lake stores the sensor data in its raw, sometimes inaccurate state. A data warehouse is where the data is stored in its cleaned state whereby the data is legible and can be read by machine learning algorithms. It is these models that make the predictions about the future states of the tools, and can be relayed to the organisation to see what needs to be done with the tools.<\/p>\n
Taking these pro-active measures ensures your operations not only run smoothly and disruption-free, but they increase ROI due to quicker output and less time spent unnecessarily fixing the equipment. According to Deloitte, predicting failures can increase equipment uptime by 20%,<\/a> so you\u2019re not spending money on maintenance, and you\u2019re getting more value for your money through keeping the equipment for even longer. The fourth industrial revolution is well and truly here, Davra would love to discuss with you<\/a> it\u2019s array of opportunities on your business today.<\/p>\n Brian McGlynn, Davra, COO<\/p>\nAuthor<\/strong><\/h2>\n