Protecting machines with prediction

With a global market of US$326.81 billion in 2021, the cement industry is one of the oldest and most complex manufacturing industries in the world. It is characterised by high-energy intensity as well as high-volume, continuous production.

Cement manufacturing plants are composed of a wide variety of components including stacker and reclaimers, ball mills, crushers, kilns, clinker cooler & dryers, roller press and silos, among others involved in crushing, blending, heating, cooling, and grinding. A catastrophic fault in any one of these pieces of equipment can cause serious downtime, resulting in financial losses to the manufacturer by way of process failures leading to lost batches, as well as danger to workers.

Rotary machines in a cement production plant operate in a dusty and corrosive/erosive environment which naturally leads to a reduction in the useful lifespan of these assets when compared to other industries. The equipment often runs at dynamic loads and speeds and faces failures and downtime due to the repetitive nature of the tasks being performed. Additionally, higher demand for efficiency and quality in production output has put an ever-increasing strain on maintenance teams to prevent unplanned downtime, which has a significant negative impact on the bottom line of the organisations. It is, therefore, essential to reduce the likelihood of unplanned downtime as much as possible. This can be achieved with the help of condition monitoring, Artificial Intelligence (AI) and Internet of Things (IoT).

AI and IoT in cement manufacturing

Equipment failures in a cement manufacturing plant result in severe consequences for the manufacturer, with a loss of production, increased maintenance costs, and danger to worker safety. With the rising demand for efficiency and quality in cement production, manufacturers are turning to digitalisation in order to optimise their operations.

The use of IoT devices for monitoring the performance of machinery has been a significant step towards optimising cement manufacturing. Manufacturers have been moving towards IoT adoption due to its potential to provide smoother operations coupled with advanced machine-to-machine communications. The capability of IoT in conjunction with AI to collect machine health data, detect patterns, and identify the optimal performance metrics in challenging applications is what is driving its adoption in the cement manufacturing industry.

It allows operators to obtain complete visibility of their overall manufacturing process and then combines it with monitoring systems to get an integrated view of plant operations that incorporates machine health.

Advancements in IoT technology have meant that massive volumes of data are now at the disposal of maintenance professionals, and they are in search for tools that can help extract meaningful and actionable insights from these datasets.

Signal processing, automation algorithms, and artificial intelligence are advanced tools with the potential to parse through massive amounts of complex data collected from these machines and provide the critical information needed to improve maintenance activities. How various pieces of the software tool kits are combined together is crucial and the more advanced systems start with ensuring clean signals that process data streams through algorithms such as Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN).

Once signals are processed, different algorithms, automation, and AI can be used in myriad ways.

The fundamental goal is to ensure that the right tools are used to analyse different kinds of data from the machines in a truly automated fashion that is scalable and then able to provide real-time insights on machine health with high accuracy that does not lead to false positives or miss key events or faults. When implemented appropriately, it can detect even small changes in the machine performance, and suggest root causes that can be used by maintenance and operations teams to prevent equipment failures and avoid unplanned downtime.

AI and IoT have brought about a paradigm shift in asset maintenance and management in cement manufacturing with the ability to offer critical information about the health of the machines and tailoring maintenance routines to fit the need of each equipment set. The ability to view the machine conditions in real-time, ensures that maintenance and reliability professionals witness fewer surprises and can also avoid problems altogether by enabling earlier preventative action.

These kinds of systems were previously only economical on less than 15% of equipment in industrial operations but the costs have dropped dramatically while the performance of the automation and algorithms has increased markedly. This is leading to much wider and faster adoption.

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Read the article online at: https://www.worldcement.com/special-reports/22082022/protecting-machines-with-prediction/