First edition of the energy podcast
Perhaps the most common application of machine learning in the Internet of Things (IoT) is predictive maintenance. Simply put, predictive maintenance means analyzing data from sensors on machines and equipment to predict maintenance needs in advance. This, in turn, saves both time and money while providing the conditions to work more sustainably.
Globally, the market for predictive maintenance is growing by close to 40 percent per year and is estimated to be worth around SEK 235 billion in 2024¹. The development is driven by a number of factors. From cheaper sensors and computing power to increased digitization and focus on sustainability. Higher demands on reliability also contribute to development, not least in the energy sector where all types of interruptions can quickly have major consequences, both for companies and private individuals.
¹ IoT Analytics Research, Predictive Maintenance Market Report 2019-2024
Traditional maintenance work is based on principles from the childhood of the industry. It includes a large proportion of manual work and scheduled maintenance efforts. It also requires physical presence and the use of our sins to determine the need for maintenance.
The possibility of working with predictive maintenance instead means that much of the maintenance work is changing. By using connected products, sensors, AI, and data analysis, the maintenance work can be performed optimally and with a significantly higher degree of automation.
– To meet the requirements for cost-effectiveness, reliability, and sustainability, companies must prioritize correctly. The work must be devoted to value creation to a greater extent, not based on traditions and manual work. With modern maintenance and connected products, you come from the former, reactive way of working. Instead of carrying out the maintenance action when the outage or failure is a fact, it prevents and can be planned for the effective time, comments Jan-Eric Nilsson, CEO of Gomero, specialists in helping companies in the energy sector to improve the efficiency of their operations and act proactively on sustainability issues.
In addition to the use of sensors and advanced measurement technology, predictive maintenance is based on the use of algorithms to predict the best timing of maintenance efforts.
– By using both data analysis and process support, there are very good opportunities to streamline most types of maintenance work. Today, our customers are in the energy sector but predictive maintenance can be used in almost every industry, Gomeros Jan-Eric Nilsson continues.
– At the same time as technological development has created entirely new conditions for maintenance work, the needs have become clearer. If we take electricity grids as an example, continued urbanization and increased electrification will place very high demands on a stable and reliable infrastructure. There is no room for interruption. Here it becomes very clear that predictive maintenance and the development of measurement and communication systems in combination with AI can contribute to increased reliability. The environment is also a winner when physical maintenance visits can be minimized. Smarter and more efficient maintenance also helps to increase the life of existing plants and equipment, concludes Jan-Eric Nilsson.