6 trending technologies in 2023 used to apply predictive maintenance

This rings especially true for maintenance strategies because ever-emerging trends continuously improve how we approach and apply maintenance.

In this blog, we will touch upon six technology trends that influence the further evolution of predictive maintenance and how you can use them.

Industries employ AI, machine learning, and deep learning to analyze massive volumes of real-time data and anticipate asset breakdowns by finding systematic failure patterns. To assess the state of the equipment, they compile data from in-machine sensors and past service reports. These systems then notify operators of specific components that could require maintenance or replacement.

This is one of the primary benefits of AI-driven predictive maintenance, as it detects problems before they get critical, reducing the costs and time of unplanned maintenance. Moreover, AI can improve forecasting accuracy and speed.

Even though AI within predictive maintenance is in its infancy, we can see how the correct usage lowers overall downtime and leads to financial benefits. Sensorfy’s solutions continuously learn how to correctly make predictions with the data we gather by applying AI models. Our case study on monitoring automatic doors is an excellent example of how that works.

Vibration sensors send information to the cloud, and in the cloud, an algorithm calculates the moment the door may fail. As a result, our customer can now schedule maintenance well in advance and prevent unplanned downtime. This helps to guarantee a safe and continuous people flow.

Internet of Things (IoT) solutions link real-time data on vibration, operational temperature, and supply voltage to data in the cloud. As more and more companies embrace it, it has become a rapid-growing industry. Rightfully so, because IoT-based predictive maintenance results in more effective equipment utilization, increased safety, and enhanced production processes.

Moreover, the technology keeps improving, leading to lower costs of measuring sensor values, resulting in an improved global communication infrastructure. In addition, it allows for more standardization of several protocols and faster adaptation.

At Sensorfy, we apply IoT solutions to all our clients. Take our sensors on railway tracks, such as in the case of Vossloh: Sensorfy developed an intelligent sensor system consisting of one or more sensor devices, which accurately detects and registers all movements and displacement of railway sleepers, as well as some other variables.

All this data is collected and processed in a Gateway that forwards the information to the Cloud for further processing. Through real-time monitoring of the rail switches’ condition, Vossloh can detect switch malfunctions before they happen. This gives them optimal response time to schedule maintenance operations and prevent unplanned downtime.

Systematic condition monitoring is essential in preventing equipment from breaking down. Measurements range from microcracks to corrosion, vibration, temperature, and other factors. Its occurrence assesses the degree of equipment degradation. Various techniques are used, such as vibration monitoring, heath measuring, robot inspection, and infrared thermography.

This technology progresses fast. Sensors are increasingly lower priced, making them more widely applicable. Also, the accuracy improves as advanced chips can measure more precisely. To top it off, new developments have reduced their energy consumption, making it possible to connect the sensors to smaller batteries that will last for years.

A tangible example of how to apply the smallest of sensors for an extended period and measure the tiniest of vibrations is the Vencomatic Group. This solutions provider for poultry husbandry uses Sensorfy’s specially designed electronic eggs with several motion and vibration sensors to register all movements of a regular egg during its journey on the conveyer belt.

The data extracted from this process creates a clear image of the weak spots on the conveyer belt. This enables Vencomatic Group to take the proper measures to smoothen the travel of fresh eggs.

As the infrastructure for big datasets continues to grow, we have more and more calculative power at our disposal, enabling advanced algorithms to make predictions. You can already witness this occurring with innovative technologies like ChatGPT, inspiring amazement and improving with each data enter.

Now, when it comes to predictive maintenance, its systems substantially benefit from analyzing unstructured data, including sensor readings, historical records, and environmental variables. Data and cluster information are now combined via analytical tools, and big data platforms also raise the transparency of system health issues. Moreover, data storage has become cheaper, and we can store bigger datasets for extended periods. This enables successful prediction and precise modeling by searching for patterns in massive datasets. These data-driven projections provide helpful information that might speed up and improve the accuracy of corporate decisions.

However, not all data is relevant or provides a continuous input stream. In that case, there’s no need to send every piece or change in data to the cloud. After all, sending data to the cloud takes a lot of energy from the sensor battery.

An exciting way to go about that is by processing data on the edge. This so-called edge processing means that a part of the analysis is directly done on the device. Therefore, it will only send the data necessary for maintenance. Especially data that doesn’t change very fast, like humidity. This can be sent to the cloud in batches with long intervals. How often a sensor sends data to the cloud depends on how fast the variable changes and the specific application it is used for.

Although not a widely applied practice (yet), augmented and virtual reality can be game changers in the future to simplify asset inspections. Technology like this makes on-site inspection, recording failures, and paperwork follow-up much more accessible. Using headsets and smart glasses, technicians can see and use historical component data on-site.

AR also collects data from equipment sensors to navigate predictive maintenance programs. Immersive technologies thereby enhance real-time viewing and enable early defect detection.

But it’s not just that. With skilled tech people being harder to find, one could use this technology to allow less trained personnel to execute more complex work. Guided by experts, somebody can virtually look over the shoulder and lead the way for the employee on-site.

Essentially, a digital twin is a digital copy of a physical asset. It uses many virtual pieces of procedures and sensor data to imitate a physical operation as accurately as possible. It serves as a monitoring tool for the physical process it is virtualizing.

By running the digital twin through different scenarios, you can analyze the outcome and base your decisions on these findings to assess the results of deployments in the real world.

It’s a cliche to say that technology moves fast, but that doesn’t make it more accurate. Existing and widely applied solutions like AI, IoT, measuring technologies, big data, AR, and digital twins evolve rapidly and thus remain trending.

In these cases, staying up-to-date and ensuring you’re working with the latest allows you to deliver your best work. The opportunities these solutions provide are numerous, and what we discussed is just the tip of the iceberg.

But don’t let it overwhelm you; there’s a world of possibilities in the fast-moving world of technology that you can benefit from.

We have seen that machine learning offers multiple options for condition monitoring to facilitate predictive maintenance. A successful implementation requires the combination of IoT expertise, domain knowledge and data science.

Do you want to start with predictive maintenance and need an introduction to analytics? Get in touch with us.