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Recognising a problem before it arises

03 March 2024

Tom Cash explains how the role of condition-based maintenance can helps maintenance engineers recognise defects before a problem strikes.

A Forbes report claims that manufacturers face an average of 15 hours downtime per week, despite the fact that food processing plants can, typically run equipment for 16 to 20 hours a day. 

Condition-based maintenance (CBM) is a strategy that monitors the actual condition of an asset to decide what maintenance needs to be done. Using sensors, it enables a strategy of maintenance only being performed when specific indicators show decreasing performance or upcoming failure. 

Checking a machine for these indicators could include non-invasive measurements, visual inspection, performance data and scheduled tests. Condition data can then be gathered at specific intervals or continuously – which can be achieved when a machine has internal or retrofitted sensors. 

The primary goal of CBM is to optimise equipment maintenance practices by monitoring real-time data and responding to the actual condition of assets. It aims to prevent unplanned downtime, reduce maintenance costs and extend equipment lifespan by conducting maintenance activities only when necessary. 

Although similar, CBM differs from predictive maintenance in its approach. While, both strategies involve real-time data and analytics, CBM is a broader concept that covers various maintenance strategies driven by equipment condition. 

Predictive maintenance, on the other hand, specifically relies on data analysis and machine learning to predict when equipment failures are likely to occur. As the name suggests, it focusses on predicting when equipment failures might occur, with the aim to pre-emptively address issues. 

That’s not to say that CBM cannot include predictive maintenance as one of its components. However, it encompasses other techniques, such as preventive and corrective maintenance, based on observed conditions. 

Therefore, the question food maintenance engineers should ask is, what strategy best suits my requirements? CBM, for example, is suitable for equipment impacted by variations in operating conditions, such as temperature, pressure or flow rates, however, is valuable for predicting failures in equipment critical to maintaining specific processing conditions, like heat exchangers or refrigeration systems. 

CBM for food safety
Food safety problems can have many root causes. A significant number of incidents reported to the Rapid Alert System for Food and Feed (RASFF), which is established by the European Union to ensure a high level of food safety and protect public health, are caused by contaminations with extraneous material. 

Such material includes metal pieces coming from broken equipment, grease or detergent remnants. Indeed, heavy metals represented the fourth most often notified hazard category in the RASFF from 1980 to 2016. 

Contamination is arguably the biggest risk to any food and beverage manufacturing organisations, so it is important to look at new technologies, such as smart sensors, to ensure food safety as well as improving overall equipment effectiveness. 

Monitoring the condition of bearings and metal detection equipment that impact food safety eliminates the risk of metal parts ending up in the food. As mentioned above, an advanced and very welcome development is the use of sensors, which trigger timely cleaning to avoid contamination risks. These tools allow engineers to identify where the choke points are in production lines proactively, rather than reactively. 

Tom Cash is Director of Siemens parts supplier, Foxmere.


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