The steel industry is robust, complex and runs on processes that leave no room for error. Every downtime costs money. Every deviation affects quality. And every inefficiency ticks up in energy consumption.
We’ll take you through proven real-world examples from the steel industry involving IoT sensors—from foundries to hot rolling mills. In this article, you’ll discover how companies are using specific applications of IoT sensors to reduce downtime by up to 70%, lower maintenance costs, and improve product quality. Ready for a smart factory? Read on.
In foundries where steel is cast, sandblasting machines are indispensable. They continuously process sand to create, break down, clean, and reuse molds. However, maintenance at fixed intervals is often inefficient: it leads to unnecessary work or misses developing defects.
What is needed to detect early problems in sandblasters?
With Vibration sensors (Neuron Vibration) Data on the condition of the sandblasters is collected in near real-time. By analyzing this data, normal patterns become visible and deviations are immediately recognized. Deviations in these patterns indicate potential malfunctions. Thanks to set thresholds, deviations are detected in a timely manner, allowing maintenance to be performed in a targeted and efficient way.
Graph: Practical example of vibration sensors on sandblasters in the steel industry
In the above case, the vibrations were stable for a long time, but began to increase considerably after June 19. The system sounded an alarm, warning the operators of an impending failure.
The result? Why invest in vibration sensors for sandblasters:
In steel mills, numerous blower fans operate to blow air into furnaces and exhaust fans to safely remove fumes. These installations are often difficult to access and are rarely checked manually. Nevertheless, they are crucial for a stable and safe production process.
This is precisely why they are ideal for getting started with digitalization and predictive maintenance.
With vibration sensors (Neuron Vibration), deviations in vibration levels are automatically detected. As soon as the values rise, operators receive an immediate notification. This allows them to intervene before a malfunction occurs.
Graph: Practical example of IoT vibration sensors on industrial fans
Why invest in industrial fan IoT sensors?
In steel mills, fans and conveyor belts often run on transmission belts. These belts are susceptible to wear, skew, and misalignment. What begins as a small deviation can quickly lead to slipping, motor overheating, and ultimately plant failure. Because these components are often out of sight, problems are only noticed when it's too late.
The consequence?
❌ Unexpected downtime
Overheated engines
💸 Accelerated wear of bearings and belts
Reduced overall system efficiency
Fortunately, there is a solution that does look ahead.
With Neuron Ampere and vibration sensors, the behavior of the motors is continuously monitored. A decrease in current consumption, for example, can indicate a worn belt that is slipping. Vibration sensors detect misalignment or material buildup, which can point to improper alignment.
Graph: Practical example of Neuron Ampere and vibration sensors on transmission belts
A steel producer noticed an anomaly in its power consumption. Inspection revealed that the belt had started slipping. Thanks to timely replacement, motor damage was prevented, and production continued to run.
Why invest in IoT sensors for conveyor belts?
Neuron Ampere and consumption sensors monitor power consumption changes and indicate when inspection is needed. If you see a drop in power, it could be a sign that the belts are worn and need to be replaced.
In steel plants, HVAC systems (like gas scrubbers and exhaust fans) play a crucial role in ensuring air quality, safety, and emission control. But what happens when filters become clogged or a leak occurs in the system?
A clogged filter or a leak in the ventilation duct is often invisible, but can have major consequences:
These problems often arise gradually and are only noticed when it's too late.
Solution: Predictive maintenance with differential pressure sensors in HVACs
TheNeuron Differential Pressure Sensorcontinuously monitors the pressure difference across filters and ventilation ducts. As soon as the system detects deviations, operators receive an immediate warning. This allows them to perform targeted maintenance before damage occurs.
Why invest in HVAC monitoring with IoT sensors?
In steel mills, hundreds of electrical control cabinets are in operation – often in dusty, hot, and difficult-to-access environments. If a cabinet is not properly closed or the temperature rises too high, malfunctions or even fires can occur. This has direct consequences for safety and the continuity of the production process.
The biggest risks:
These risks are often invisible, but predictable - with the right sensors.
Solution: Neuron Cabinet Safety
TheNeuron Cabinet Safetyis specially developed for industrial environments such as the steel industry. This smart IoT sensor combines two crucial functions:
The sensor measures the temperature and door status every 3 seconds. In case of critical deviations, an alert is sent immediately. This prevents damage to installations and increases the safety of your personnel.
Graph: Practical example of preventing malfunctions in cabinet enclosures using IoT sensors
During a production shutdown over the Christmas holidays, the temperature in a cabinet unexpectedly began to rise. The cause? A clogged filter on the circulation fan. Thanks to the sensor, the problem was detected early – even before production restarted.
In steel mills, machines often run for thousands of hours per year. To carry out maintenance timely and efficiently, insight into the number of operating hours is crucial. Yet, recording this data is frequently done manually – a process that is slow, error-prone, and inefficient.
The risks of manual registration:
This leads to an increased risk of failures, unnecessary costs, and safety hazards.
What added value does the IoT Hour Meter offer in the steel production industry?
TheNeuron IoT energy meterautomatically records the operating hours of an installation – without mechanical parts and insensitive to magnetic fields. As soon as a machine starts, the counter begins to monitor. The data is transmitted wirelessly to the Neuron Cloud and can be linked to maintenance software via an API.
Why invest in IoT sensors for hour meters?
Real-world example: automatic recording of operating hours with an IoT hour meter
This graph shows the number of operating hours of an industrial facility per day, measured over a week. Thanks to theNeuron IoT energy meterthe operating hours are automatically recorded and wirelessly transmitted to the cloud – without manual intervention.
The data shows that the installation was fully utilized on Mondays and Wednesdays (8 hours), while less or no production occurred on other days. These insights make it possible to precisely schedule maintenance based on actual usage, rather than fixed intervals.
Result:less downtime, less unnecessary maintenance, and maximum utilization of personnel and resources.
special coating that is essential for the quality of the final product. If the temperature rises too high, this coating can be damaged, resulting in costly scrap production and downtime.
What's at stake?
Solution: Neuron PT100 temperature sensor for hot rolling steel coils
TheNeuron PT100 sensorContinuously monitors the temperature of the rollers and shafts. As soon as a critical value is exceeded, the maintenance team receives an immediate warning. This allows the process to be stopped or adjusted in a timely manner – before damage occurs.
Value:
Practical example: temperature monitoring of a hot rolling mill roll
This graph shows the temperature fluctuations of a hot rolling mill roll over time. The peak in the measurements indicates a moment of overheating, which was signaled early thanks to the Neuron PT100 sensor. This allowed the roll to be replaced in time, preventing damage to the coating and rejection of material.
Why invest in IoT sensors for hot rolling?
Conveyor belts are the backbone of many industrial processes. But what happens when such a belt unexpectedly fails? Think of delayed deliveries, production lines grinding to a halt, or even dangerous situations on the work floor.
Why do conveyor belts fail?
Most failures are caused bywear on bearings, gearboxes, or drive motors. Factors such as heat, humidity, and pollution accelerate this process. And it often happens at the worst possible moment.
Consequences of failure:
Solution: Predictive maintenance with IoT sensors for conveyor belts
MetNeuron Vibration - Ampere SensorsThe condition of conveyor belts is continuously monitored. The sensors detect deviations in vibrations or power consumption – signals of initial wear or misalignment. This allows you to intervene before problems occur.
Why invest in IoT sensors for conveyor belts?
Real-world example: residue buildup visualized with IoT
At a steel producer, using theNeuron sensorsaccurately tracked the operating hours of a conveyor belt. Based on this data, the lubrication interval was performed exactly on time. The graph shows a clear increase in residue buildup over time – an indication that maintenance was needed.
Thanks to the timely warning, wear on the conveyor belt was prevented, and production continued uninterrupted. Without this monitoring, it could have led to unexpected downtime and costly repairs.
Real-world example: Preventing misaligned belts
This graph shows the vibration data of a fan driven by a transmission belt. In the days leading up to October 5th, a gradual increase in vibration levels was observed. Thanks to the Neuron Vibration sensor, this was signaled early. Inspection revealed that the transmission belt was starting to run askew – a common cause of wear and energy loss. By intervening in a timely manner, the belt could be realigned, preventing damage to bearings and the drive, and allowing the installation to continue running without interruption.
In the steel industry, compressed air is indispensable. It is used for pneumatic actuation, cleaning, and overpressure protection of critical equipment. However, compressed air systems are notorious for their inefficiencies:invisible leaks, pressure loss, and unexpected malfunctionsleading to high costs and risks.
What can go wrong?
These problems often arise gradually – and are only noticed when it's already too late.
Solution: real-time monitoring with IoT sensors
Neuron flow and vibration sensorscontinu monitor the performance of compressors and other components of the compressed air system. They detect deviations in consumption, vibrations, or pressure – signals of leaks, wear, or impending failure. This allows you to intervene proactively before the system fails.
Why invest in IoT sensors for compressed air systems?
Real-world example: combined vibration and temperature monitoring on a compressor
This graph shows the combined measurement ofvibration levels (g)entemperature (°C)on an industrial compressor, over the period of January 6th to February 14th. The green line shows the vibrations, while the blue line tracks the temperature.
During multiple peak moments, it is clearly visible that increased vibrations were accompanied by a rising temperature – an indication of possible wear or imbalance in the drivetrain. Thanks to real-time monitoring withNeuron sensorsthe maintenance team was able to intervene in time and unexpected downtime was prevented.
In industrial environments like steel mills, lubrication systems are crucial for machine reliability. However, many lubrication points are still manually checked – a time-consuming and error-prone approach. Especially inMist lubricatorsenautomated grease pumpsA small leak or blockage can have major consequences.
❗ What can go wrong?
Solution: Neuron potentiometer + level sensor
Through aNeuron potentiometerto combine with aLiquid level sensor, the oil level in mist lubricators is continuously monitored. In the event of a sudden drop – for example, due to a leak – a warning is immediately sent.
Practical example: Real-time monitoring of oil level in a mist lubricator
This chart shows the trend of the oil level in a mist lubricator reservoir over several days. From Tuesday to Friday morning, the level remains stable at around 8,500 units. Around midday on Friday, the oil level suddenly drops to almost zero – a clear indication of a leak or unexpectedly high consumption.
Thanks to the combination of aNeuron potentiometer and level sensorthis deviation was detected immediately. This allowed the maintenance team to intervene quickly and prevented equipment from running without lubrication, which could have resulted in damage or downtime.
For rotating machines with grease supply, there is now a solution with theLube flow meter linked to theNeuron pulse counter. This measures accuratelywhenenhow muchmilk is administered.
Advantage
Complete insight into the grease supply per lubrication point – essential for audits, maintenance planning, and fault detection. No grease = damage, and that can now be completely prevented.
This graph shows the fat supply in a rotating system, measured via two data channels:
The step-shaped lines indicate when and how much fat was administered. On October 11 and 12, it can be clearly seen that the fat supply is occurring in controlled amounts. Thanks to the coupling of theLube flow meterto theNeuron pulse counterIt is precisely recorded whether the correct volume of fat was delivered at the right time.
Result:full transparency in the lubrication process, better maintenance planning, and preventing damage from under- or over-lubrication.
Why invest in IoT sensors for lubrication systems?
Do you want maximum uptime and control over maintenance?
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