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IIoT-based predictive maintenance – A mission critical need for manufacturing

Industry 4.0 continues to gain momentum across every industrial and manufacturing segment. This revolution is built upon three primary technologies: Big Data, Edge Computing and…

IIoT-based predictive maintenance – A mission critical need for manufacturing

Industry 4.0 continues to gain momentum across every industrial and manufacturing segment. This revolution is built upon three primary technologies: Big Data, Edge Computing and the Internet of Things (IoT). As the adoption of IoT devices continues to grow, many organizations are switching to edge technology because of its advantages over legacy cloud solutions. One of the key advantages of edge computing is real-time predictive maintenance. In a predictive analytics solution, Artificial Intelligence (AI) is combined with Business Intelligence (BI) to monitor the operating condition and predict when to perform maintenance on that asset.

What is Predictive Analytics?
Predictive analytics uses statistical algorithms and advanced analytics combined with AI techniques to predict future outcomes based on historical and current data patterns. Organizations use this method to benefit possible future events by using predictive modelling to take maintenance decisions before a disruptive event. This technique imports data from the targeted asset synthesizes it and combines it with different data sources. Once a large amount of data is cleaned, the data analysis is initiated to recognize patterns and trends. In simple words, using Artificial Intelligence and Machine Learning technique, a machine can predict future events.

What is Predictive Maintenance? A subset of predictive analytics, predictive maintenance is the process of utilizing data analysis to predict future outcomes. This technique is used to recognize potential faults in machines and processes. Manufacturing and service industries need to improve the performance of their assets. As per the report by a leading publication, spending on IoT-enabled predictive maintenance will reach 12.9 billion by 2022 compared to $3.4 billion in 2018.

Benefits of Predictive Maintenance:

An AI-enabled predictive maintenance solution comes with numerous competitive advantages as compared to legacy maintenance processes.
1. Improved Machine Lifespan: By identifying problems, machines can be serviced even before the problem occurs. Also, with a constant study of the machine, the AI solution prevents any significant damage from occurring, consequently improving the overall health of connected equipment and uptime its average lifespan.
2. Increased Production: With the ability to constantly monitor a machine’s performance, one can avoid unscheduled downtimes and improve operations throughput. This not only improves the machine’s health but also enhances the quality of the production.
3. Minimize Maintenance Costs: With the help of IoT sensors, it becomes easy to detect anomalies and repair them before the problem becomes irreversible. This minimizes the chance of operational setbacks due to unplanned machine downtime. A report by McKinsey suggests that a predictive maintenance application can minimize maintenance costs by 25%. On the other hand, Deloitte believes it can reduce machine breakdowns by 70%.
4. Reduction in Downtime: A predictive maintenance solution can cause approximately a 45% reduction in downtime. The analytics provide insight on faults and require repairs so you can schedule them accordingly. This helps companies to effectively optimize their resource schedules or schedule maintenance outside of operation hours.
5. Improved Benefits: The data collected from the IoT-based solution helps businesses make practical and calculative decisions regarding machine management. This can improve manufacturing value by enhancing the overall equipment effectiveness and the production volume. This can also decrease replacement or repair costs. Businesses are leveraging IoT-based predictive maintenance to improve value and minimize costs.

The Future of Predictive Maintenance

Although cloud computing can support predictive analytics systems, organizations gain a crucial advantage by refining data analytics and processing speed and performance through edge computing. A predictive maintenance solution performed at the edge minimizes data storage costs along with real-time analytics and low latency. IoT devices and sensors gather data frequently, meaning these IoT-enabled solutions work with enormous data.

When we implement such solutions through cloud computing, vast data gets shared over the network to the cloud. While the load on the internet continues to grow, the cost of networking will increase as well. Predictive maintenance solutions, run on the edge analyze the data on-premise in real-time to minimize the amount of data shared on the cloud, saving businesses money on cloud storage costs.

About Infinite Uptime

Infinite Uptime is transforming the industrial health diagnostics space with a Digital First approach. We provide comprehensive solutions around Machine Diagnostics, Predictive Maintenance and Condition Monitoring to the top engineering and process industries globally. We promise to deliver maximum Machine Uptime, minimize Factory Disruption and elevate Equipment Reliability for a stellar factory performance.