Businesses need their critical assets or equipment to run at the highest efficiency to get a better return on their investment. These assets could be electric utility poles, turbines, chillers, elevators, engines, etc., costing hundreds or thousands of dollars to purchase and set up.
Many businesses follow a corrective maintenance strategy in which parts of the assets are utilized until they become dysfunctional. At the surface of it, this strategy seems cost-effective, but in reality, it costs downtime, high maintenance needs, labor expenses, and more. Some businesses practice preventive maintenance measures in which the life of each part of the asset is calculated to perform maintenance activity before the failure or outage.
With technological advancements like the Internet of Things (IoT) and Digital Twins, businesses are moving towards Predictive Asset Maintenance. It is preventative maintenance using past data and big data analytics to identify anomalies and predict asset downtime or failures.
At the core of developing any effective predictive maintenance algorithm lies sensor data, which is used to train a classification algorithm for fault detection. Meaningful features are extracted from this data in a preprocessing step and fed to a machine-learning algorithm for predictive maintenance.
However, obtaining data from physical equipment in the field during fault conditions is often not feasible due to the risk of catastrophic failure and equipment damage. Intentionally generating faults under more controlled circumstances through physical testing can be time-consuming and expensive.
To address these challenges, digital twin technology, one of the major disruptive technologies in Industry 4.0, has emerged as a game-changer. This blog explores how various industries utilize digital twin technology for predictive maintenance.
Understanding Digital Twins
A digital twin is a digital/ virtual representation of a physical device, object, or service created by collecting real-time data from sensors, devices, and other sources. They provide a real-time replica of the physical machine and generate data, such as asset degradation, which the predictive maintenance algorithm can use.
Digital twins are made of numerous technologies – from IoT sensors to 3D CAD files to potentially augmented reality (AR) visualization; it’s the product of an ecosystem of data communication. It can duplicate processes to gather data so that one can predict how it will perform in the future.
In sectors like manufacturing, energy, and automotive, digital twins are proving to be a powerful tool for predictive maintenance, enabling companies to detect and diagnose problems in advance, optimize performance, and prevent downtime.
Let’s explore how different industries can use digital twins for predictive maintenance.
Digital Twin Applications Across Industries
The digital twin can collect and analyze data from sensors on the physical machine, such as temperature, vibration, and pressure, to monitor its condition and predict when maintenance is required. It identifies patterns and anomalies in the data and alerts maintenance teams to potential issues before they result in downtime or costly repairs.
A contextual model of the machinery through the digital twin in manufacturing can help understand the root cause of a problem. One can gain complete clarity about the present and future conditions of the machines. There will also be recommendations to help operators assemble an industrial object, perform quality controls, and improve the manufacturing process.
Automotive & Aerospace
From the system design phase to the production line, digital twins can help the automotive and aerospace industry to perfect every stage of development and improve the integrity of the vehicles. The digital twin can collect and analyze data, such as oil temperature, pressure, and engine speed, to detect anomalies or deviations from the norm. It helps identify potential issues, such as an oil pressure decline or a temperature rise. These insights can help manufacturers optimize their maintenance schedule and reduce downtime.
Automotive manufacturers like BMW use digital twinning for process control, efficient resource management, and predictive maintenance.
Energy, Power & Oil
Energy and oil companies can use digital twin technology for predictive maintenance by creating a virtual replica of their physical assets, such as turbines, pumps, and pipelines. The digital twin model is then connected to sensors and other data sources to monitor real-time performance. For instance, if the digital twin model of an oil pipeline detects an abnormal vibration pattern in a section, it can alert the maintenance team to investigate and fix the issue to avoid a leak or a rupture.
Enel Group, a leading Italian multinational manufacturer and distributor of electricity & gas, uses digital twin technology for asset maintenance and inspection. The company has partnered with iMerit to get high-quality data for asset inspection and predictive maintenance. View case study
Medical device manufacturing is an excellent fit for digital twin technology due to the importance of accurate planning and forecasting and the impact of breakdowns on patient care and revenue. For instance, if an MRI system breaks down, appointments will need rescheduling, and hospitals cannot collect checkup fees. Emergency repairs are also more expensive than routine maintenance. Digital twin technology enables medical device manufacturers to detect and address potential issues before they arise, reducing unplanned downtime and maintenance costs.
Scientists are also developing a digital twin of the human immune system. In the future, doctors may have an up-to-date digital twin on file, including any information from the health provider, like vaccine status, blood tests, etc.
The adoption of Digital Twin technology has been surging in recent years and will continue to evolve rapidly. With advancements in artificial intelligence, the Internet of Things (IoT), and other emerging technologies, we can expect to see new applications and uses in once unimaginable cases.
One of the most crucial factors in building and implementing digital technology is the quality of training datasets. High-quality data ensures these systems can make accurate predictions, detect patterns, and generate meaningful and actionable insights.