IoT in Industry: Revolutionizing Manufacturing

In the bustling world of industry, where machines hum and supply chains weave intricate webs of production, a silent revolution is taking place. It’s not about the clanking of gears or the roar of engines; it’s about the whisper of data flowing seamlessly through the veins of manufacturing and supply chains. Welcome to the era of IoT, where interconnected devices are transforming the way we make things and move things.

Imagine a factory floor bustling with activity. Workers scurry from machine to machine, ensuring that each component is assembled with precision and care. In the past, monitoring these machines and predicting maintenance needs was a tedious task, often relying on manual inspections and guesswork. But with IoT, machines are now equipped with sensors that gather real-time data on performance, humidity, temperature, and vibration. This data is then transmitted to a central hub, where algorithms analyze it to detect anomalies and predict potential failures before they occur.

Let me tell you about a case study that illustrates the power of IoT in manufacturing, but it has some time challenges. In a previous experience, a leading manufacturer of baked products tried to implement an IoT solution to monitor their production line. We start with one machine, a spiral cooler. This equipment had a belt with the possibility of being monitored on each lap with a proximity sensor. The PLC is monitoring each of these sensors in real time. If one sensor stops, meaning the belt could have a failure, the PLC stops the central motor, presents an alarm, and the maintenance team is called to fix the issue. This action prevents the belt from suffering more damage and allows the company to save time and money in the process.

To increase reliability and be more precise, we install IoT sensors to gather other information in real time but also to analyze its vibration. The vibration could give us information on the behavior of any state of the machine. Each failure has a typical "curve" that could be associated with it. We could see, in real time, each sensor and its behavior in each location. On each failure, the model learns, and when the model sees the same pattern, it sends a signal to the PLC before something happens.

One process is more efficient in terms of prediction; the other is efficient in terms of vigilance after the failure. One solution allows to stop the machine without any damages, just the opportunity cost related to production.

While the PLC sensors are more effective in the learning process, the vibration system could be more effective in the future, where each failure has its own graphic and the model is complete. How long does it take? That is the key question. To have all failures in the model easily, it can take 20 to 30 years. So, the learning process is a cost-and-patience investment in this case. But also, if you have well-implemented preventive maintenance, the machine won't have enough failures to learn.

By equipping their machines with sensors and connecting them to a centralized monitoring system, we would be able to predict failures before they happen. With real-time insights into machine performance, we could proactively address issues before they escalated, saving time and money in the process. The issue is that you might need to model all failures to be proactive, but by being proactive in your maintenance and PMs plan, you might never encounter a failure to upload in the program.

In conclusion, the IoT is revolutionizing manufacturing in ways we never thought possible. By harnessing the power of interconnected devices and real-time data analytics, companies are improving efficiency, reducing costs, and enhancing the overall quality of their products and services. The journey towards Industry 4.0 may still be in its infancy, and will need more analysis on specific solutions, but with IoT leading the way, the future looks brighter than ever for the world of industry.

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