Iron & Steel Industry and Predictive Maintenance Applications

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.

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.

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.,

Pharmaceutical Industry and Predictive Maintenance Applications

Pharmaceutical Industry is one of the largest fields of production in the world. With an incredibly demanding clientele, challenging development and production processes and high volumes of production, Pharmaceutical Industry is forced to be a pioneer to research and implement new technologies into their production lines. These innovations are vital to keep up with the constantly increasing needs of the world. Efficiency, quality, sustainability and safety are some of the main traits focused on by the innovators in the field of pharmaceuticals. In order to achieve improvements in these traits, production lines of pharmaceutical companies must be in correlation with innovative technologies regarding every aspect of the production. Maintenance is one the fields that require attention to ensure production lines are efficient, sustainable, safe and able to produce high quality products. In this blog post the main focus will be on predictive maintenance applications that are fitting for the needs of the pharmaceutical industry.

A large variety of machines are used in the pharmaceutical industry. Processing equipment include agitators, centrifuges, blowers, capsule equipment, chillers, pulverizers etc. Post production equipment include CAM Blister Packing Machines, Bottling Lines, Counters, Sealers etc. All of these equipment works with small size products in a highly complicated, fast and precise manner. That’s why components used in these machinery are compact, precisely machined and purpose built. If any one of the critical components in these complicated machines were to fail, quality of the product can be heavily affected and all of the production line might face an unforeseen stoppage. In order to prevent such stoppages and malfunctions maintenance teams need to implement predictive maintenance technologies. Main elements providing and conveying the movement to the machinery are rolling elements, gearboxes, servo and induction motors. Predictive maintenance applications can be performed through many different applications such as thermal monitoring, lubrication analysis, ultrasonic inspection, vibration analysis, electrical analysis etc. Among these different analysis approaches vibration and electrical analyses are the most viable ones due to the ease of application and affordability.

Electrical analysis is one of the predictive maintenance applications fitting to the needs of the pharmaceutical industry. Especially on the components such as servo and induction motors used for the generation of precise and continuous movement to the production line. IoT based data acquisition devices paired with current transformers and voltage dividers can provide precise data about the energy consumption and efficiency of the equipment. These measurements can be analyzed to diagnose many different modes of malfunctions such as stator, rotor and connector problems and misalignment, unbalance and bearing failures before they result in a serious failure.

Vibration analysis is widely used in industry for the predictive maintenance of rotating machinery. Vibration data is usually collected through accelerometer instruments. This data can indicate many different modes of malfunction if collected and analyzed correctly. Many generic failure types such as mechanical looseness, unbalance, bearing failure, cavitation, gear failure, lubrication fault, belt and pulley fault etc. can be detected through vibration analysis. Sensemore AI powered predictive maintenance applications can identify anomalies indicating the root causes of malfunctions months prior to the occurrence of the malfunction.

Foreseeing failures, estimating remaining useful life and eliminating catastrophic malfunctions through early detection and corrective actions is the key to create an industry with higher efficiency, quality and sustainability.

History and Future of Maintenance Culture

There are many different explanations of what maintenance is in the technical field. It is a wide concept that contains tests, measurements, replacements, adjustments, repairs etc.that aim to increase the overall health of a system. Even though this broad explanation of maintenance is true for all of our history, maintenance culture is filled with different approaches and procedures for different parts of humankind’s technological timeline. In this blog post, the main focus will be these different maintenance cultures throughout the history and the future of maintenance.

For the majority of our existence, humanity relied on a maintenance approach called Corrective Maintenance. This approach is the earliest one among the maintenance concepts. Malfunctions are addressed and systems are maintained when a failure comes to existence. This approach is solely reactive since systems generated before the industrial revolution contained no condition monitoring or failure analysis process. Due to the smaller volumes of production and less demand from the market, this corrective maintenance approach did not create much of a problem for the pre-industrial revolution era of production.

After the Industrial Revolution maintenance turned into a higher importance issue for plants around the world. Especially widely used boilers started to become a threat for workers due to the commonly occurring failures resulting in explosions. Industry started to implement periodic evaluations on these boiler equipment in order to ensure safety in the work environment. This new method of ensuring the health of the system marks the emergence of a new maintenance approach called Preventive Maintenance. Preventive Maintenance holds the aim of eliminating the possibility of malfunction through periodic maintenance processes at heart. With the increasing demand from the market and growing production volumes this approach started to spread from boilers to a wide variety of machinery in various plants.

Throughout human history wars have always been a driving force behind innovative technologies. After World War 2 industry started to focus more on reliability and availability of systems since it would grant the machinery the vital ability of being able to operate in short notices successfully. This resulted in the birth of a new maintenance approach called Proactive Maintenance. Proactive Maintenance relies on fault analysis and condition monitoring. Fault analysis methods enable users of a system to evaluate a failure alongside its root causes, conduct criticality analysis and eliminate conditions resulting in a malfunction. Condition monitoring systems inform users on vitals of the system through sensors connected to critical equipment. The constant inspection and flow of information enables the operators of the system to observe any differences in the condition of the machinery and to take action before a malfunction occurs.

Nowadays with the increased availability of various sensors and the emergence of the Industry 4.0 concept, condition monitoring technologies became more popular in production sites. The ability to acquire large amounts of data from the machinery facilitated the creation of a new maintenance approach called Predictive Maintenance. Predictive Maintenance utilizes large amounts of data gathered through condition monitoring applications, and conducts analysis to understand the mode of malfunction and creates models to predict the root cause and the remaining useful life of machinery.

Future of maintenance holds infinite possibilities with the constant developments in the machine learning field and AI technology. Solutions such as Sensemore products use AI models to perform these analyses in an automated manner and predict the root causes of malfunctions before they occur. Using AI algorithms to go through large quantities of data gathered from systems, continuous condition monitoring through precise sensors and conducting root cause analysis is the future of maintenance culture.

What is Fault Tree Analysis?

To ensure the safety and reliability of a system, reliability agents such as engineers and maintenance personnel need to understand a fault and its impact on the system thoroughly. This understanding requires detailed analysis of faults and other undesired operational states of the machinery. There are many different types of approach and methodology when it comes to analyzing faults of a system. In this piece the main focus will be the failure analysis method known widely as Fault Tree Analysis.

Fault Tree Analysis is a failure analysis method created by Bell Laboratories in 1962 to be used in the aerospace field. This method focuses on the faults a system might encounter, relationships among these failures and interactions between other subsystems and elements.  In this analysis format the undesired state , a failure , is placed on the top of the tree as the main focus point. By working in a backward manner related subsystems components, machine elements and operational actions are examined. All this analysis is later compiled under a tree-like structure with a mapping created by symbols of events and logic gates such as AND, OR etc.

Fig. 1 Fan System Failure Diagram

What are the steps of FTA?

Defining the fault to analyze:

Defining a critical fault is essential to conduct a FTA. Undesired event, a fault, must be chosen by taking criticality, complexity and impact on the system into consideration. This is a vital stage since the top element in the FTA is unique and analysis conducted specifically for one failure. 

Understanding the system completely:

Once the focus point, failure, is chosen any related element should be studied thoroughly. Actions, subsystems, components, environmental elements etc. must be noted to obtain an understanding of the failure alongside any related input. Occurrence possibilities of the events are calculated and indicated for every event related to the undesired event.

Create and evaluate the fault tree:

After studying the system, construction of the tree representation begins. Every event and state leading to undesired state of fault is listed and existing relations between the conditions are represented by using AND or OR gates. Fault tree is evaluated for any improvements and all the possible hazards resulting in the undesired event is obtained. 

Taking action according to the fault tree analysis:

After the Fault Tree representation is complete and all the necessary studies are conducted actions must be taken to increase the reliability of the system and to decrease the probability of potential hazardous states leading to the undesired event.

An Example of FTA

In this example, failure of a fan system is determined as the undesired state. Main possible causes of the malfunction are Fan Element Malfunction, Component Failure and Motor Failure. All of these sub level malfunctions like Bearing Failure, Motor Failure, Broken and Stuck Impellar are taken into evaluation and mapped on the Fault Tree Diagram. 

Fig. 2 Failure Mode Diagram

What is Reliability and FMEA?

Ability to prevent failures on a system and a product is the indicator of reliability of the process. Reliability is a key element of a production process that ensures both the production line and the resulting product is existing in the desired state. This element of reliability is created and maintained by the meticulous work of reliability engineering agents such as engineers overseeing the process, maintenance and production personnel.

Concept of reliability is implemented in two distinct forms as proactive and reactive reliability. Proactive reliability relies on foreseeing possible failures and taking appropriate precautions to prevent the occurrence of the failure. Reactive reliability is more of a failure management approach where failures are addressed and eliminated after they come to existence.  A state of ideal reliability is created by detecting and eliminating any possible failure before any occurrence through proactive reliability. Achieving such forecasts for a productive reliability application is possible through conduction of failure analysis. 


A Failure Modes and Effects Analysis (FMEA) is often one of the first steps you would undertake to analyze and improve the reliability of a system or piece of equipment. FMEA is a failure analysis method where every component, assembly and subassembly of a system is observed and studied to determine any possible failure each component might face. Each element is analyzed for its failure modes and impacts of such failures on the whole system. 

In order to evaluate the risk level and the required precautions, assessed elements are given ratings on probability of occurrence, severity of the failure and detection method.


In order to give a component a probability rating analysis,FEM calculations, literature research and comparison to previous failures.

Ratings and their meanings are as such:

Fig. 1 Probability Rating Criteria


In order to give a component a severity rating user has to study the expected and experiences.

Fig. 2 Severity Rating Criteria

Detection Method

The ability and the method of detection for a possible failure is another important aspect of FMEA. Components are assessed according to the ease of failure detection.

Fig. 3 Detection Method Rating Criteria

Additional Assessment Criteria

Potential failure mode , potential cause, mission phase, local effects of failure, next higher level effect, system-level end effect, detection dormancy period, actions for further investigation are all categories of information needed for a complete FMEA.

Impact of Sensemore in Reliability Applications

Solutions developed by Sensemore enables their users to monitor the health of their machinery , constantly keep conditions under supervision, detect and classify different operation and failure modes , predict possible malfunctions before the occurrence and provide a higher level of failure detection ability. 

For FMEA applications, Sensemore solutions provide a great advantage by constantly gathering data about the operation regime, failure modes, root causes of malfunctions and many different types of data such as vibration , temperature, current, voltage etc.. By distinguishing different operation and failure modes Sensemore products enable a more precise assessment for the probability rating part of FMEA. Also through the higher perception power Sensemore products provide their users, rating indicating the difficulty of fault detection is decreased.

Through predictive maintenance and condition monitoring applications such as Sensemore products, conducting proactive reliability analyses such as FMEA is easier and more accurate.