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Improving Your Maintenance With IoT

Improving Your Maintenance With IoT

Digitalization is the most recent trend among businesses and industries all over the globe. The process involves introducing new technologies into existing production lines to increase efficiency and production while minimizing costs and waste. Technologies such as artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and others work together and provide some revolutionary possibilities.

Predictive maintenance is one of the main benefits of introducing IoT solutions into production. The system collects operational data from all physical assets in real-time. It identifies faulty machinery and predicts maintenance efforts very accurately. Let's see how IoT improves maintenance efforts in more detail.

IoT - The Best Approach to Manufacturing

Maintenance can be approached from many different angles. However, traditional methods such as reactive, planned, or scheduled maintenance are not efficient and come with long downtimes in production. On the other hand, predictive maintenance is much more accurate than any other method. It's possible only due to IoT sensors placed on every asset. The sensors generate real-time operational data, sending it to the centralized AI-powered system that monitors the data and makes accurate predictions.

As ML algorithms become smarter, their ability to forecast potential hazards, machinery breakdown, and other problems with industrial equipment become even more accurate. For example, IoT sensors collect temperature, vibration, supply voltage, etc. The system analyzes all details and uses predictive analytics to estimate the current state of components and predict when they will need repairs.

Core Technologies Behind Predictive Maintenance

The system has to adopt a few essential technologies that combine their features to produce accurate results for predictive maintenance to work. The method rests on big data, cloud computing, ML, wireless connectivity, edge computing, and, of course, IoT. The process of digital transformation in manufacturing can be complex and hard to set up. However, the benefits outweigh all the downsides. Once the adoption is complete, manufacturers and other industries get a long list of benefits

The first and most important step when introducing IoT solutions into an existing process is to find ways to connect all assets and embedded devices into a single wireless system. That involves placing sensors, antennas, batteries, and other components all over the operations. 

Once the sensors are in place, they can generate operational data on all machines, motors, drivers, and other elements. The data is then filtered to extract only essential information, and the remaining bits of information are sent to a cloud-based AI solution. The AI in charge structures the data and looks for gaps in the operation. It analyzes operational data such as vibrations, temperature, and other performance indicators to identify problems and propose the best solutions. 

The essential step here is to ensure that the system collects and analyzes the right data. If everything works correctly, the system can easily predict the condition of every machine by comparing its performance indicators with the standard. If the indicators are not within normal levels, the AI can predict when the machine will break down. As the ML becomes smarter, it will make more accurate predictions in variable conditions, allowing manufacturers to organize their maintenance efforts and other key processes with unprecedented accuracy.

Benefits of Predictive Maintenance

Manufacturers keep adopting IoT systems far and wide because they provide a long list of benefits. Predictive maintenance is just one of them, but it affects the entire operation on multiple levels. Here are some benefits you can expect to get from predictive maintenance.

Reduced Maintenance Costs

All machines and devices require maintenance at some point. However, maintenance efforts can be costly, especially if the machine breaks down completely. Traditional methods are expensive, and they can damage your production and ROI. With IoT-based predictive maintenance, you can ensure that all repairs are scheduled on time before a catastrophic breakdown. The approach will remove unplanned downtime due to broken machinery altogether, saving you a lot of time and money.

Asset Reliability

Whenever a machine breaks down completely, the entire production line has to stop, drastically reducing machine utilization. As you can imagine, that harms profitability and ROI. IoT solutions can ensure that your machines are in perfect working order at all times. Moreover, the system will recognize the slightest change in operational data scheduling maintenance efforts so that you avoid machine failures altogether. 

Increased Machinery Lifetime

Predictive maintenance uses real-time monitoring to keep a close eye on all equipment at all times. That approach allows the system to identify components that need replacing before a catastrophic failure accurately. The system will schedule maintenance automatically, ensuring that all repairs are completed on time, extending the life of your machinery.

Improved Compliance and Safety

IoT sensors allow you to monitor the temperature, RPMs, voltage, and other KPIs on every machine in real-time. Then, whenever the system identifies a hazard or a risk to workers, it will alert the manager immediately, giving all employees enough time to resolve the issue before things get more complicated.

Steps Towards Adopting Predictive Maintenance Into Manufacturing

It's clear that IoT and predictive maintenance help extend the life of all assets, increase their efficiency, and reduce downtimes and overall costs. However, these benefits are only available to those who correctly complete the integration of IoT technologies. You have to take specific steps when adopting an IoT solution to streamline maintenance efforts. Follow these steps and introduce predictive maintenance into your manufacturing plants.

List All Assets That Require Predictive Maintenance

The first and most important step is to differentiate assets that need predictive maintenance from those that don't. The best way to do that is to identify all machines that require regular maintenance and have the biggest impact on downtimes. 

Once you identify the assets you want to apply predictive maintenance efforts to, you can rank them by importance based on historical data on downtimes and repair costs. In other words, apply IoT solutions to those machines that break down the most and then extend the practice to other assets over time.

Choose the Right Software For Your Operation

New IoT solutions and AI-driven software are being released all the time. Many different software tools can help you improve operational efficiency and introduce predictive maintenance into manufacturing. However, it's important to know that not all are made for the same application. 

The best approach is to get some computerized maintenance management system or CMMS designed for the market category you belong to. Then, read user reviews, compare features, and do serious research to understand how different software works and their benefits. Only then will you find the best solution for your operation. 

Consult Software Experts for More Advice

Choosing the best software for your needs can be difficult, especially if you're not a software expert. That's why you should always consult an advisor to discuss your business needs and budget before you start the digital transformation. They will analyze your system and recommend the best IoT solutions to provide you with the most benefits.


The bottom line is - IoT technology for manufacturing processes is completely redefining the process itself. It is especially efficient for streamlining maintenance efforts using operational data. Predictive maintenance can save your company a lot of money in the long run by minimizing downtimes due to equipment repair. Moreover, you will be able to eliminate machinery breakdowns, reduce spare parts costs, and extend the life of all assets.