The cement production business has become one of the essential industries in the world. In reality, the cement sector will become significantly more critical in manufacturing in the coming days. This decade has seen unprecedented growth at the center of most economic advancements and worldwide expansion. According to Fortune Insights, the cement industry will expand by 5.1% from $326.80 billion in 2021 to a valuation of $458.64 billion in 2028.

As a result, keeping the facilities operational while incurring minimal maintenance expenditures has become critical to surviving in this market. Unfortunately, in the first decade of the twenty-first century, first-generation cement plants were characterized by some bad actors, notably mill motors and critical equipment like bearings, feeders, and lubricating systems, falling out of the orchestra, resulting in over 30 percent downtime – raw mills and finish mills were running at just under 80 percent and 70%, respectively. So it’s no surprise that cement mills are under pressure to maintain their processes and assets.

Maintenance basics in the cement industry

As you know, implementing a predictive maintenance strategy can save significant money in the cement sector. However, saving operating costs is partially tied to keeping equipment running longer and more efficiently, reducing downtime risks, and improving preventative maintenance procedures. Therefore, maintain vigilance in designing your maintenance routine.

High-performance infrared sensors may offer real-time temperature data on the rotating kiln shell. These assist in managing the operators in the control room to keep the kiln shell under constant monitoring for potential faults. In addition, the shell thermal map display is used to indicate when critical parameter alert thresholds, such as hot spot identification, tire slide out of range, or coating loss, are achieved. Aside from that, the cement industry incorporates additional significant elements that help the maintenance manager schedule preventative activities and improve kiln lifetime.

How can predictive Maintenance be implemented in the cement industry?

Predictive Maintenance is based on the collection of relevant data from sensors, CMMS, and cutting-edge digital technologies such as artificial intelligence (AI) and also Internet of Things (IoT) (Industrial Internet of Things). At its most basic, predictive Maintenance is based on sensors mounted on, around, or within a machine – say, on the edge of a rotor – that log a substantial value reflecting the machine’s performance.

Why is asset maintenance in cement plants required?

Cement factory asset maintenance is vital because:

  1. High replacement and repair costs.
  2. The possibility of occupational safety hazards and accidents.
  3. Over-maintenance of machinery leads to wear and tear.
  4. Operating conditions are challenging.
  5. A dynamic environment necessitates prompt decision-making.
  6. Enable continuous monitoring/remote monitoring and control for increased agility and resilience.

Unplanned downtime in even one of these devices may wreak havoc on the current operation, and increase maintenance costs due to reactive maintenance, and jeopardizing not just efficiency and quality but also the health and safety of on-site people.

Use of AI and Machine Learning in Predictive Maintenance

Predictive Maintenance has modernized its methods. Nowadays, artificial intelligence is the primary pillar of this method. As previously said, predictive Maintenance is based on data received from sensors that record vital metrics about a machine in real time; the challenge of estimating uptime is represented in many ways using different methodologies. For example, some systems treat fault prediction as a classification issue, while others try to reduce the mean error between projected and actual uptimes. AI generally enables predictive maintenance systems to simulate human-like capabilities, infer a malfunction, and alert your maintenance experts to potential problems.

Predictive Maintenance requires little more than an informal mathematical computation on when machine conditions need repair or replacement so that maintenance may be conducted precisely when and how it is most efficient. However, machine learning eliminates most guessing and allows plant managers to focus on other responsibilities. Machine Learning enables you to perform automatic data processing on a sample dataset or your own. The ML model identifies possible equipment problems and recommends the next steps.

AI and IoT in the cement industry

To perform predictive Maintenance, large amounts of data need to be processed, and complex algorithms must be implemented, which can’t be done locally. On the other hand, a cloud-based solution allows storing and analyzing terabytes of data and the concurrent execution of machine learning algorithms on several computers to foresee possible risks and identify when industrial equipment is likely to malfunction. Therefore, a cloud-based AI-powered predictive maintenance system requires a well-thought-out architecture that can be easily implemented.

Major discrete manufacturers are utilizing AI-based predictive Maintenance to check the condition of spindles in milling machines, for example. However, they are prone to breaking, and their care is costly. In an AI-based predictive maintenance system, data collected from ultrasonic and vibration sensors attached to the spindle could be used to forecast future damage. In addition, data analysis aids in identifying weak spindles before they break.


The cement industry is on the verge of a digital revolution spurred by increased demand, fierce rivalry, and cement manufacturers are on the constant lookout to reduce their carbon footprint. The market for the cement industry’s assets, processes, and people to perform at peak levels has never been greater. Predictive Maintenance may assist minimize machine breakdowns and the related unexpected downtime, as well as the specific quality of the output cement and the cement plants’ OEE (Overall Equipment Effectiveness). It increases machine availability and performance while lowering maintenance and replacement component expenses. Most significantly, it provides resilience and adaptability during uncertain periods through remote monitoring and proactive maintenance when it is most needed.

Interesting reads:

Case Study: Early Detection and Diagnosis of Kiln and Cooler Efficiency Loss in Cement Manufacturing. Read the cement manufacturing case study.

Blog: How AI and ML Is Transforming The Cement Industry

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