Predictive maintenance analytics along with cyber-physical systems, the Internet of things and cloud computing is amongst the most talked about in the current trend of automation and data exchange in manufacturing technologies leading to Industry 4.0. In this blog, we throw light on what exactly predictive maintenance is.
We live in a world of fear. The fear of breakdown is nothing less than a nightmare for any manufacturing company, the production managers know best. Any machinery breakdown is costly, not just in terms of money, but also in time, the use of materials as well as the human resource involved in the process. Contributing to breakdown expense is that equipment failure usually seems to occur when least expected and when that particular piece of machinery is most needed.
They not only hamper the manufacturing or construction schedule. They create a lot of stress for the operator, supervisor and the entire crew who are interdependent on all the machinery on the job to function properly as a team. The loss of operation of one single piece of machinery brings the complete site to a halt and it remains that way until repairs or replacement of the equipment are made. Other equipment and resources continue to absorb costs while being idle and waiting for the cause of the breakdown to be established. The cost of downtime, caused by even a single machinery is huge. IT giants, over the period of time have developed innumerable preventive mechanism, wherein measure are taken beforehand so that the chances of breakdowns are minimised. However, the chances did remain and so the need for a better process still remained.
So, predictive maintenance came into play. Predictive maintenance (PdM) techniques are designed to help determine the condition of operating equipment in order to predict when maintenance should be performed. The chances left for the breakdown in preventive maintenance are no more. The prime purpose of predictive maintenance is to allow convenient scheduling of corrective maintenance, and to eliminate unexpected equipment failures. The key is “the right information at the right time”. By knowing which equipment needs maintenance, maintenance work can be better planned and initiated in an effective manner i.e planning for replacing part, spare or the machinery itself. The main components of predictive maintenance analytics are data collection and analytics that leads to early fault detection. Therefore, Predictive maintenance has been considered to be one of the driving forces for improving productivity and one of the ways to achieve “just-in-time” manufacturing.
Infinite Uptime is the first company to introduce a product that, through high-frequency edge computing, delivers predictive maintenance analytics on the spot with real time instantaneous data analytics. This is possible through the IDE – Industrial Data Enabler. It is a solution that can digitize any machine within a minute. The IDE is a compact, portable, battery-operated handheld device for productivity optimization and automated predictive maintenance in the industry. Through IoT (Internet of Things), it detects anomalies and defects in real time through on-board LED indicators and warns an operator immediately. The backup battery allows wireless testing and installation and never stops monitoring, even in a power outage.
Infinite Uptime is a vertically integrated solution in the industrial IoT space for operative and predictive analytics. This analytics is displayed on its IDAP (Industrial Data Analytics Platform).
- Operative analytics means analytics of machines in the industry, characterizing their operations, efficiency and effectiveness.
- Predictive analytics means predictive maintenance, prediction of anomalous conditions well in advance, and reliability assessment which includes process reliability, process management and overall asset reliability.
Obtaining and calculating high-frequency mechanical data is extremely hard, made possible and easily usable only with the IDE through its patented technology. Edge computing allows IDE to make sense of mechanical data in an easy and viable way. Edge computing being faster than cloud computing, shows real time high-frequency analytics. The analytics is based on data from a combination of multiple high-frequency IoT enabled sensor sources that can communicate through wifi, bluetooth, LoRa and GPRS. This in turn provides frequency based analytics on the IDAP which is the best way to pinpoint the source of an anomaly. IDAP shows tailored data analytics that is most relevant to the customers in the discrete and process manufacturing industries.
In a nutshell, equipment failures will happen if you are not prepared for it and preventive maintenance is not enough. Hence, predictive maintenance becomes the need of the hour. Predictive maintenance strategies are based on the combination of operational and predictive maintenance analytics, thus enabling the prediction of machine failures before they occur. IoT and advances in analytics are driving market adoption with users of the technology reporting tremendous efficiency gains. Basically, in Industry 4.0, Infinite Uptime is a stethoscope for machines.