Industrial IoT for Predictive Maintenance
For many years, manufacturers have been performing a time-based approach to the equipment maintenance. They used to consider the machinery age as important aspect for planning the maintenance routine. The older the equipment the more repeated maintenance procedures require to be carried out. As per ARC group stats only 18% of equipment has failed due to machinery age, while 82% of failures occur randomly. It proves that a time-based approach is not that much cost-efficient. To avoid ineffective maintenance routine and costs that accompany it, manufacturers takes benefit of Industrial IoT predictive maintenance and data science.
This means that in the coming years we will see more business problems, even those that are not machine maintenance specific become reorganized as predictive maintenance.
Specifically, predictive maintenance and IoT solutions will solve problems we never even thought about — once we learn to creatively remake those problems in a way that can be solved using IoT data and predictive analysis. Today we’ll inspect the topic of predictive maintenance in the Industrial IoT and how these advancements are raising industry-wide efficiencies.
Predictive maintenance for industry 4.0 is a method of preventing assets failure by monitoring production data to recognize patterns and estimate concerns before they happen. Previously factory managers and system operators carried out scheduled maintenance, other processes and regularly repaired machine parts to prevent downtime.
Predictive maintenance has rapidly emerged as a leading Industry 4.0 use case for manufacturers, factory managers and asset managers. By implementing IIOT technology to analyze asset nature, optimize maintenance schedules and gaining real-time alerts to operational risks, enables manufacturers to reduce service costs, enhance uptime and improve production throughput.
What are the objectives of predictive maintenance?
In most cases today, objectives of predictive maintenance is divided into two outcome:
1. Improving Production efficiency:
Based on readings from a machine, production efficiency can be improved either by increasing the time when machines are running through predictive maintenance or by predicting the number of goods that will pass or fail in quality inspection. That enables manufactures to reduce maintenance costs, expand equipment life, decrease downtime and enhance production quality by addressing problems before they cause equipment failures.
2. Improving Maintenance efficiency:
Analyzing for future failure enables maintenance to be designed before failure occurs. That improves maintenance efficiency of equipment and of entire system.
How does IIOT predictive maintenance works?
For predictive maintenance to be carried out following are the important components that are essential:
1. Sensor
  Data collecting sensors situated in machine.
2. Data Communication
  The system that allows data to travel safely in between the analyzed asset and stored data.
3. Central data store
  It is the central data store where asset data from operational technology and business data from information technology are stored, monitored and processed for the further operations.
4. Predictive analytics
  Analytics algorithms when applied to the whole data to identify patterns and then generate the insights in the form of alerts.
5. Root cause analysis
  This tool is used by process engineers to check the insights and determine the preventive action to be performed.
Production asset data is flowing from sensors to central store using various IoT protocols and gateways. The business data from ERP and MES systems combined with manufacturing process are fused into the central data store which provides surroundings to the production asset data.
After that predictive analytics algorithms are applied that provides insight in the form of alerts or dashboards to reduce downtime. And then it is exploring by using Root cause analysis software.
To apply a predictive maintenance system efficiently, machine operators need to map the parameters of failure for machines and create a blueprint for their connected system that includes sensors and manufacturing assets, business systems, communication protocols, gateways, predictive analytics, cloud and visualization. Predictive analytics are implemented to the data generated by machine and the system blueprint data in order to estimate conditions of upcoming failure.
What is a condition-based monitoring?
Condition monitoring and Predictive maintenance both ensures permanent accessibility of safety of critical equipment with minimum and near about zero interruption. Data obtained through Condition Monitoring includes essential information about the current state of a system. But its value is not limited to assessing equipment’s condition at a given time. Its development can be used to predict how equipment will perform and how it might decay – and to arrange maintenance according to these expectations. This is what exactly predictive maintenance is.
It is a process by which the condition of a machine is constantly analyzed by looking at pre-defined equipment parameters. It enables tracking of system patterns which might indicate equipment failure. Early indication of failure prevents major failure.
This ensures durability and smooth running of the equipment operations. Condition Monitoring is an essential aspect of Preventive Maintenance; it ensures allows you to know when your equipment is near to the end of its life. Condition Monitoring also allows for the operations team to plan for its gentle replacement.
Predictive Maintenance Benefits:
Customers and manufacturers get a wide range of business benefits from predictive maintenance:
1. Decrease maintenance time:
Generated reports and proactive repairs alone decrease maintenance time by 20–50 percent and reduce overall maintenance costs by 5–10 percent. It will automatically save the manufacturer and customer’s time as well as money.
2. Enhance efficiency:
Insights generated by analytics software will enhance overall equipment effectiveness by reducing unnecessary maintenance that enlarge products life and allow root cause analysis to discover issues ahead of failure.
3. New revenue streams:
Manufacturers can analyze industrial predictive maintenance by providing analytics-driven services for their customers that involves predictive maintenance dashboards, maintenance schedules or a technician service before parts need replacement. The ability to provide services to customers depends on data presents an opportunity for generating revenue streams and an exponential growth for companies.
4. Customer satisfaction:
Customers will get automated alerts regarding when parts need to be replaced and maintenance actions will be taken by the machine engineers to boost satisfaction and provide a greater measure of predictability.
5. Competitive benefit:
Predictive maintenance increase company branding and add more value to customers satisfaction. That enables products to stand alone from the competition and allowing them to provide continuous benefit in-market.
The opportunities that we have to upgrade businesses on this significant a scale are too valuable to explore. We at hIOTron provide IoT Solutions over various industrial architectures and concerns to reshape many businesses, across almost every industry.