By merging data analytics and foresight, industrial IoT predictive maintenance protects your machinery and services from downtime. This strategy forecasts your equipment’s future, giving you solutions to prevent failures beforehand. Embracing predictive maintenance in IoT takes this process to an even higher level of operational excellence.
The global predictive maintenance market will surge from $5.7 billion in 2024 to over $49 billion in 2032 — all thanks to the Internet of Things. With interconnected IoT sensors and devices in place, businesses can now proactively manage their equipment in real time through an iot-based energy management system and allocate their resources more efficiently.
In this post, we’ll guide you through achieving the above improvements by diving into the top 5 IoT predictive maintenance use cases. As an experienced IoT development vendor, WebbyLab will detail the challenges, considerations, and best practices of IoT implementation. We’ll also share oT predictive maintenance case studies, including our project, 2Smart Standalone.
The Internet of Things impacts equipment upkeep significantly. Yet, to make the most of this tech, you should learn about the main components of predictive maintenance for IoT devices and IoT solutions:
IoT sensors for predictive maintenance and devices are at the core of IoT-powered predictive maintenance. Embedded within equipment and machinery, they collect data on various parameters. Such gadgets track temperature, pressure, humidity, vibration, and other metrics, providing real-time insights into the equipment’s condition and performance.
IoT sensors and devices generate colossal amounts of data, and these datasets must be properly collected and processed. Predictive maintenance systems require robust storage. In this regard, cloud-based platforms stand out as the best solution because of their flexibility and almost limitless storage capacities, especially in the context of predictive maintenance in IoT. Additional components include edge computing that enables data processing closer to the data source, which can be facilitated through custom IoT development services.
Ensuring seamless communication between devices and sensors is critical in IoT-based predictive maintenance systems. Only with connectivity can the gadgets exchange data over networks. Communication protocols like MQTT, CoAP, or Zigbee are necessary in this respect.
An IoT-powered predictive maintenance system requires predictive analytics to get insights into the machinery’s upkeep. It involves leveraging advanced algorithms and, often, ML models to study the data sensors collect. With IoT analytics services, businesses can repair or replace equipment parts before disruptions occur.
Despite popular belief, IoT predictive maintenance extends beyond production plants. While this innovation is indeed promising for this sector, other industries might also benefit from it. So, let’s look at the best five use cases of predictive maintenance with IoT.
The Internet of Things revolutionizes global logistics and redefines how transportation systems work. And we’re here to prove that.
IoT sensors attached to trucks, containers, ships, and vehicles monitor cargo status, temperature, humidity, and location in real time. Predictive maintenance, in this case, allows businesses to optimize routes, prevent freight damage, and speed up delivery times.
Besides this, companies typically leverage IoT-based predictive maintenance systems for fleet management. They attach IoT sensors to vehicles and gather data about engine performance, tire pressure, and fuel efficiency. By analyzing the collected datasets through predictive maintenance algorithms, they proactively schedule maintenance and keep their fleet up and running at a minimum cost.
This innovation is also suitable for air transport. Airlines benefit from gathered data on engine operation, system performance, and overall aircraft health, which can be used in predictive maintenance for IoT devices to schedule maintenance services.
Other use cases of Internet of Things predictive maintenance include the manufacturing industry. Companies use IoT sensors to monitor their machinery and equipment conditions and detect anomalies in vibration, temperature, and other critical parameters. The predictive maintenance system, in turn, alerts maintenance teams about possible issues before they lead to breakdowns. This optimizes manufacturing processes and reduces downtime.
The energy sector is another great candidate for IoT in predictive maintenance. IoT sensors for predictive maintenance are lifesavers in the real-time monitoring of turbines, transformers, and generators — essential components of power plants, grids, and utility systems. Similar sensor-driven systems are also widely used for weather monitoring in IoT, helping track environmental conditions with high precision. These tiny devices observe various parameters, from vibration and electrical currents to water quality and temperature. They allow companies to identify signs of equipment degradation and prevent accidents.
IoT-based predictive maintenance is reshaping patient care and medical equipment management. Hospital units use IoT sensors to monitor critical medical equipment, like MRI machines and ventilators. It helps keep patients safe and reduces costly downtime.
Besides equipment, IoT for predictive maintenance extends to health monitoring, where wearable devices track heart rate, sleep patterns, and other metrics. By evaluating those parameters, healthcare providers can prevent unexpected health problems and improve patient outcomes.
IoT use cases extend to our living spaces — from smart homes to cities. In smart homes, everyday devices like refrigerators, washing machines, and security systems are seamlessly connected to the IoT network. This connectivity allows people to optimize energy use and minimize inconvenient failures.
Beyond individual homes, IoT’s impact scales up to smart cities. IoT sensors integrated into buildings can monitor various systems: ventilation, air conditioning, power distribution, and security. This real-time data collection reduces disruptions on a larger scale.
Though the use cases of predictive maintenance using IoT are extremely promising, you should also be aware of the possible concerns. Let’s explore the main ones:
The IoT system’s data is a double-edged sword. It helps with real-time equipment maintenance updates, but insufficient protection can compromise and distress users. To avoid such uncomfortable situations, consider the following matters:
When implementing Internet of Things for predictive maintenance, it’s critical to consider how exactly the new tech will work within your existing system and equipment. Ensure you cover the following issues:
Your IoT-based predictive maintenance system is only effective if it uses reliable sensors. Considerations in this regard might include the following:
As already mentioned, IoT predictive maintenance solutions gather vast amounts of data. It may be overwhelming to manage all of those. But fear not, and consider the following:
While describing the best examples of predictive maintenance with IoT, we mentioned several industries that benefit from this innovation. Now, we’ll go into further detail and provide real-world examples of successful Internet of Things implementation.
As a part of the 2Smart Standalone project, WebbyLab experts have implemented solutions for automated load testing of devices to facilitate their timely maintenance and understanding of critical loads.
We brought this solution to life through automation scripts. One decent example is a load-testing scenario with a convenient UI for heat pump parameter configuration. We connected IoT devices to the system, with each scenario emulating the load on them. Our administrators monitored device behavior and logs for calibration and improvement.
Each device underwent a rigorous load testing process before installation, ensuring it could withstand heavy usage conditions once deployed in the system. That’s precisely why we created a simple scenario with easy-to-change test parameters and the ability to execute tests on many devices. Alternatively, we developed custom scripts for more specific or one-time load-testing tasks.
Heat pump stress testing through 2Smart Standalone
The WebbyLab team deployed a remote monitoring system for heating devices via the 2Smart Standalone automation platform.
We moved from conventional offline monitoring, where status indicators were conveyed through light bulbs displaying issues and failures. With 2Smart Standalone, our experts enabled automated and remote supervision and maintenance of the heating system.
An example of the monitoring system for the boiler room
Detecting and fixing firmware bugs is only possible during device operation. Our team emphasizes the importance of bug logging and has enabled this feature in our 2Smart Standalone platform.
2Smart equips IoT device manufacturers with an embedded solution to seamlessly integrate cloud-connected log generation. Stored in the InfluxDB database, these logs are accessible to vendors and administrators, offering insights into device malfunctions. It allows for remote debugging and real-time assessment of device responses to interventions. When logging isn’t available, sensor data on the MQTT broker serves as an alternative source of insights.
Logging bugs in a 2Smart Standalone platform
Another real-world application of predictive maintenance with IoT emerged during the presale phase of one of our projects. Its goal was to develop a device for monitoring the maintenance status of vending coffee machines. We suggested installing a controller on the machines, which would alert users about breakdowns and notify them about the need for replenishing supplies.
Farmers optimize their operations by monitoring their agricultural machinery through IoT sensors. That’s the case with Innovation Agritech Group. The company offers innovative maintenance tools for vertical farming and aeroponics. Beyond that, they provide controlled environment agriculture solutions that involve growing plants under fully managed conditions. Their systems enable tracking and configuring water, temperature, humidity, and other parameters.
Now that you know everything about IoT predictive maintenance use cases, real-life examples, and challenges, let’s see how to implement such a system in your enterprise. It takes the following five steps:
At this stage, you should identify the equipment and services that require predictive maintenance within your company. It would be best to cover these tasks:
Collaborate with engineers and IoT device development specialists to choose suitable sensors for each asset’s monitoring requirements. Here are the tips to consider at this stage:
At this point, you may want to engage IoT experts to ensure seamless communication between devices and sensors. The tasks to handle here include:
At this stage, it’s best to collaborate with data scientists and ML engineers. These experts will help you come up with predictive models suitable for your particular requirements. Other things you want to cover are as follows:
The last step involves turning the collected, processed, and analyzed data into actionable insights. You should also consider how to integrate these upkeep predictions into your company’s workflows. Here are some tips that may come in handy:
You may also be interested in learning about the cost to build IoT prototype.
Predictive maintenance with IoT is a data-driven solution that prevents equipment failures and spares businesses from unfavorable downtimes. It benefits virtually every industry: agriculture, manufacturing, transportation, healthcare, and beyond.
The successful examples of real-life IoT predictive maintenance implementation show the viability of this endeavor. Solely through our 2Smart Standalone mobile and web platform, we could automate and simplify monitoring and maintenance for our numerous clients.
Whether you require optimization of production, enhanced healthcare equipment reliability, or streamlined logistics, WebbyLab can provide a customized predictive maintenance solution for you with our IoT-based MVP expertise. Our proficient back- and front-end development team can craft an IoT-based MVP predictive maintenance system from the ground up, or you can harness the power of our 2Smart Standalone as a dependable foundation.
So, regardless of your choice, WebbyLab experts are ready for the challenge. Contact us for a detailed consultation, and let’s enhance your operational efficiency together.
Written by:
Head of IoT at Webbylab
With a robust academic background in Telecommunication Systems Engineering, I apply my knowledge to lead innovations in the IoT domain. Starting as the first team member in the newly formed IoT department at WebbyLab, I've spearheaded its growth, fostering the expansion into embedded and hardware development alongside our core software projects. My dedication lies in pushing the boundaries of IoT technology, fostering a culture of innovation and excellence that profoundly impacts our clients' operational success.
It collects and analyzes real-time data from vehicles, sending instant alerts in case of failures or potential breakdowns. This way, transportation companies can keep their vehicles in optimal condition, ultimately preventing accidents and improving road safety.
Yes, and the most prominent of them are edge computing and 5G connectivity. One innovation enables faster data processing, while another enhances data transmission speed.
Yes, it’s highly adaptable. Businesses can customize everything for their equipment maintenance needs, from sensor selection to predictive analysis model development.
It minimizes downtime and prevents businesses from having to make unscheduled repairs. Therefore, companies can schedule maintenance tasks in advance, allocating their resources wisely.
The accuracy of IoT predictive maintenance predictions can vary widely depending on the quality of data, the sophistication of the predictive algorithms, and the specific use case. In general, when implemented properly, IoT predictive maintenance can be highly accurate, often achieving prediction accuracies above 90%.
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