Predictive Maintenance

Last Updated on July 6, 2022 by azamqasim92

The cement industry is one of the most energy-intensive manufacturing industries, using approximately 900 kWh of electricity per tonne of cement produced. As a result, the sector is responsible for around 5% of global industrial energy-related carbon dioxide (CO2) emissions.

In order to mitigate these emissions and improve its environmental footprint, the industry has set ambitious goals to reduce its specific energy consumption by 20–25% by 2030 from a 2010 baseline.

One of the key technologies that can help the cement industry achieve its energy efficiency goals is predictive maintenance (PdM). Here is everything you should know about PdM.

What is Predictive Maintenance?

Predictive maintenance (PdM) is a strategy for maintaining equipment that uses condition monitoring data to detect potential issues and schedule corrective maintenance activities before problems occur.

This helps to avoid unplanned downtime and increases equipment reliability.

PdM can be used on any type of equipment, but it is particularly well-suited to production machinery and other capital-intensive assets that are critical to business operations.

How Does Predictive Maintenance Work?

PdM relies on condition monitoring data to detect potential issues with equipment. This data can be collected in a number of ways, including visual inspections, audible sound analysis, vibration analysis, and thermographic analysis.

Once the data has been collected, it is analyzed using predictive analytics techniques to identify patterns that could indicate a potential issue.

If an issue is detected, corrective maintenance can be scheduled before the problem occurs. That way, avoiding unplanned downtime and increasing equipment reliability is possible.

The Benefits of Predictive Maintenance

Predictive maintenance has plenty of benefits. For instance, increased equipment uptime is one of the most significant advantages because it can lead to increased production and improved profitability.

By contrast, unplanned downtime can cost businesses a lot of money in lost productivity and repair costs.

PdM can also help to reduce maintenance costs by avoiding unnecessary repairs and extending the lifespan of the equipment. What’s more, predictive maintenance can improve safety by identifying potential hazards before they occur.

Last but not least, PdM generates data that can be used to improve future decision-making about equipment purchases, installations, and operations. This data can help businesses optimize their production processes and become more efficient overall.

Cement Industry Predictive Maintenance Use Cases?

Now that we’ve answered the question: “What is predictive maintenance?” it’s time to look at how it is being used in the cement industry.

Thermographic analysis is one of the most commonly used PdM techniques in the cement industry. This involves using a thermal camera to scan equipment for hot spots that could indicate an issue.

For example, thermographic analysis can be used to detect issues with bearings, motors, and electrical panels.

Vibration analysis is another common PdM technique that is used to detect issues such as misalignment, unbalance, looseness, and bearing failure. In the cement industry, vibration analysis is often used to monitor kiln drives, mill drives, crushers, and conveyors.

PdM can also be used to monitor other process variables such as pressure, temperature, flow, and level. For instance, it can be used to detect clogging in preheaters, calciner, and Cooler.

PdM can also be used to monitor the condition of the kiln shell, which is subject to wear and tear over time. Monitoring the kiln shell can help cement plants avoid unplanned downtime and extend the life of the kiln.

Anomaly Detection

One of the essential applications of predictive maintenance is anomaly detection.

Anomaly detection is the process of identifying unusual data points that deviate from the rest of the data. This can be done using statistical methods, machine learning algorithms, or a combination of both.

Anomaly detection can be used to detect a variety of issues, such as equipment failures, process abnormalities, and security breaches.

In the cement industry, anomaly detection is often used to monitor kiln performance. For example, it can be used to detect abnormalities in the kiln feed, clinker quality, and energy consumption.

Anomaly detection can also be used to monitor other process variables such as pressure, temperature, flow, and level. By monitoring these variables, cement plants can avoid unplanned downtime and improve process efficiency.

How to Implement PdM in cement manufacturing?

There are several steps involved in implementing predictive maintenance.

First, condition monitoring data must be collected from the relevant equipment. Next, this data must be processed and analyzed to extract useful information. Finally, this information must be used to make decisions about when and how to carry out maintenance.

The first step is to select the right condition monitoring sensors for the job. There is a wide range of sensors available on the market, so it’s crucial to select the ones that are best suited to the specific application.

Once the data is collected, it must be processed and analyzed. This can be done using statistical methods, machine learning algorithms, or a combination of both.

In the end, the processed data must be used to make decisions about when and how to carry out maintenance. This will typically involve setting up alerts that notify staff when a piece of equipment is due for maintenance.

It’s also important to consider the cost of carrying out predictive maintenance. PdM can be expensive, so it’s important to weigh the benefits against the costs.

In some cases, it may be more cost-effective to carry out preventive maintenance instead. That requires a significant amount of data and processing power. For this reason, it’s often necessary to invest in additional hardware and software resources.

Predictive maintenance also requires trained staff who are familiar with the relevant condition monitoring techniques. This can be a challenge for cement plants, as there is a limited pool of qualified personnel. One way to overcome this challenge is to partner with an experienced PdM service provider.

Service providers can help cement plants set up and operate their condition monitoring systems. They can also provide training and support to plant staff.

When selecting a service provider, it’s important to choose one that has experience in the cement industry. This will ensure that they are familiar with the unique challenges faced by cement plants.

Another thing to consider is prescriptive maintenance. This is a type of predictive maintenance that uses artificial intelligence (AI) to make decisions about when and how to carry out maintenance. 

It’s a more sophisticated form of predictive maintenance that can provide better results.

PdM and Machine Learning

Machine learning is a type of artificial intelligence. It’s a powerful tool that can be used to detect patterns in data that would be difficult to spot using traditional methods.

Machine learning can be used to develop models that predict when equipment is likely to fail. These models are based on historical data and the current condition of the equipment.

When equipment is due for maintenance, an alert can be sent to staff. This allows them to carry out the necessary repairs before the equipment fails. Machine learning can also be used to optimize maintenance schedules.


Predictive maintenance is a powerful tool that can be used to improve the uptime of cement manufacturing equipment.

Any successful implementation will require a significant investment in data collection, processing, and analysis. With properly trained staff and the right service provider, predictive maintenance can help cement plants improve their bottom line.

Read More: Mortar vs. Concrete: What Are the Differences?

Author bio

Rick Seidl is a digital marketing specialist with a bachelor’s degree in Digital Media and Communications, based in Portland, Oregon. He carries a burning passion for digital marketing, social media, small business development, and establishing its presence in a digital world, and is currently quenching his thirst through writing about digital marketing and business strategies for Life and Style Hub.