The iron and steel industry is one of the most important fields of production. Its products are key components of many of the concepts that our lives revolve around, such as transportation, construction, infrastructure, technological devices etc. It is a major contributor to economic growth with 2.5 trillion dollar revenue and employment, as it provides 6 million direct and 40 million indirect job opportunities. Also Iron and Steel industry is a significant contributor to energy consumption and carbon emissions globally. Steel production is energy-intensive, and the majority of energy used in the industry comes from fossil fuels, such as coal, natural gas, and oil. The use of fossil fuels in the production of steel results in the release of greenhouse gasses, such as carbon dioxide, into the atmosphere, casuing to climate change. In addition, the energy used to operate the machinery and equipment in the industry also contributes to energy consumption and carbon emissions. As the industry with the highest energy consumption and 7 to 9 percent of the world’s carbon emission (2.6 Gt CO2e, highest among the heavy industries), the iron and steel industry demands innovation and attention from humankind. As a result, technologies focusing on efficiency, reliability, safety and sustainability are welcomed by the iron and steel industry. In this blog post the main focus will be on the Iron and Steel Industry and the innovative impact predictive maintenance applications can have on the industry.

Iron steel predictive maintenance

The iron and steel industry relies on a wide range of machinery to produce various steel products. This machinery includes: Furnaces that are used to melt and refine raw materials, such as iron ore, scrap steel, and limestone, into steel, Rolling Mills that are used to shape the steel into different forms, such as sheets, plates, bars, and rods, Casting Machines that are used to cast the molten steel into different shapes, such as billets, blooms, slabs, and rounds, Air Blowers that are used to ventilate and circulate air, blast cleaning, drying and cooling, Pumps that are used to supply water, scrap metal handling, slag transportation, etc. In addition to these core machines, the industry also uses cranes and hoists to move materials around the facility, conveyor belts to transport materials, and specialized equipment for cleaning, coating, and finishing the steel products. Among this machinery, certain machines that work with metal at high temperatures, such as furnaces, casting machines, and rolling mills, are particularly critical. This is because a failure in these machines can result in significant energy losses, as they consume large amounts of energy in their operation. In addition, a malfunction in these machines can disrupt the entire production process, leading to downtime and lost profits. As a result, it is important to ensure that these machines are well-maintained and operate efficiently to minimize the risk of failure and energy losses.

Predictive maintenance is a proactive approach to maintaining equipment that involves using data and analytics to predict when equipment is likely to fail or require maintenance. By analyzing usage data and identifying patterns that suggest maintenance is needed, industry professionals can schedule maintenance at a time that is most convenient and cost-effective. This approach helps to optimize equipment usage and can result in significant cost savings by reducing the overall cost of maintenance. Additionally, by eliminating unplanned downtimes, iron and steel facilities can prevent energy loss due to the loss of heat that occurs when processes are interrupted. This can help to increase productivity and improve overall operational efficiency.

Iron steel predictive maintenance

Predictive maintenance applications can be implemented through the use of sensors and creating a condition monitoring system. These sensors can be vibration, temperature, oil, current, voltage etc. sensors. These condition monitoring techniques are implemented on equipment and machinery to track performance and detect any potential issues before they occur. For continuous processes in the iron and steel industry and the high number of critical equipment creating a condition monitoring system based on vibration analysis is the most viable choice both due to the availability of the technology and the ability to forecast possible malfunctions on rotating machinery. Another application of predictive maintenance in the iron industry is through the use of machine learning algorithms. Due to the constant nature of the processes and the ability to deploy stationary sensors enables agents of reliability to achieve a large enough data set, “Big Data”, feed machine learning algorithms. These algorithms can analyze data from sensors and other sources to identify patterns and predict when maintenance is needed. Sophisticated ML algorithms such as ones created by Sensemore can even predict the root cause of a malfunction alongside the estimated time of occurrence through equipment specific learning processes and high analysis capabilities. This allows iron industry professionals to schedule maintenance at a time that is most convenient, rather than waiting for a breakdown to occur.

In conclusion, the iron and steel industry is a vital contributor to the global economy and a major consumer of energy and carbon emissions. Predictive maintenance technologies, such as the use of sensors and machine learning algorithms, can help to improve the efficiency, reliability, safety, and sustainability of the industry by predicting when maintenance is needed and optimizing equipment usage. These technologies can help to reduce energy consumption and carbon emissions, as well as improve the overall efficiency of the industry. Implementing predictive maintenance in the iron and steel industry can also help to reduce downtime, improve product quality, and increase profits. Overall, the use of predictive maintenance in the iron and steel industry can have significant benefits for the industry, the environment, and society as a whole.


  • Kim, Jinsoo, et al. “Decarbonizing the Iron and Steel Industry: A Systematic Review of Sociotechnical Systems, Technological Innovations, and Policy Options.” Energy Research & Social Science, vol. 89, 2022, p. 102565.,