Updated: Nov 26, 2020
Predictive maintenance is the way to avoid expensive and potentially catastrophic machine failures in a manufacturing facility. Data captured through sensors throughout the manufacturing process are analysed to generate crucial information regarding any abnormal activity. This information is important to be prepared for a failure to significantly reduce the total period of downtime.
Prior to IoT's (Internet of Things) widespread adoption in production environments, the system operators and professionals would also have to arrange repairs at regular intervals to determine what requires maintenance.
Why is predictive maintenance crucial?
Half of any manually scheduled machine maintenance is deemed to be unnecessary. Given the immense amount of money, time, and effort involved in this type of maintenance, it is not surprising that many manufacturing concerns have shifted away from this approach and are transitioning to Industry 4.0.
Preventive maintenance vs Predictive Maintenance
Visual inspections and regular health checks are the basis of preventive maintenance. But this has its limitations. Regularly scheduled maintenance practices check and replace parts irrespective of condition. This makes it expensive and unnecessary.
Predictive maintenance on the other hand is tastefully proactive and is based on real-time and historical data to forecast a malfunction in the system so it can be “prevented”.
How Predictive Maintenance Works
The following are essential components of a predictive maintenance solution.
Sensors for asset data collection.
Secure data transfer to the database
Cloud/ On-Premise data storage
Data analytics and Expert systems
Classification of fault onset
Prediction of Time to failure.
Dashboards and Reports.
In order to successfully incorporate predictive maintenance systems into a manufacturing facility, users must first determine what a failure might mean for each piece of machinery. This can be determined using manufacturing assets, sensor data, communication procedures, predictive analytics, and dashboard warnings.
Using a visual interface, the technical teams will be able to visualize the production line graphically. This will involve data flow, dashboards, and system logic – where algorithms detect anomalies when they show up.
By adapting to historical data, the algorithms learn to gain insights to newer environments/data which helps them get better over time.
Benefits of Predictive Maintenance
Significant decrease in Machine Downtime – Automation of Preventive Maintenance Schedules have been shown to reduce maintenance time by 20-50% while minimizing the associated cost of maintenance by approximately 5-10% in parallel.
Maintains Performance – descriptive and predictive analytics helps improve machine efficiency without the need for excessive maintenance. Any issues identified can be scheduled for repair when the machine is not in operation. It helps forecast the asset failure prior to the occurance.
How will Predictive Maintenance be used for manufacturing?
Over the last few years, manufacturing companies have adopted predictive maintenance strategies in a number of situations, from the factory-wide deployment to the monitoring of a single vital machine component.
For companies that manufacture goods on a mass scale – such as food or toys – predictive maintenance is a great way to minimize product errors thereby eradicating waste.
For those manufacturing parts and equipment, the general usage of predictive maintenance is to set the technology for tracking and inspecting the condition of moving devices and engines. Productivity, sensor data, health, and internal wear are all tracked.
Predictive maintenance gives a manufacturers a competitive edge and further helps reduce costs, and boost efficiency in the workplace.
We at Radianarc believe it is upon us to take this technology forward and create a sustainable future by transforming every industry through automation and insight.
Our mission is to help industries in their journey towards digital transformation by building the world's simplest IIoT solution. After all, simplicity is the ultimate sophistication.
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