Beyond Preventative Maintenance
Preventative maintenance, servicing equipment on a fixed schedule regardless of its actual condition, has long been the standard approach to avoiding unplanned breakdowns in FMCG manufacturing. Predictive maintenance takes this further, using sensor data and condition monitoring to identify developing faults before they cause a failure, allowing maintenance to be scheduled based on actual equipment condition rather than a generic time interval. For manufacturers running ageing production lines, where original equipment may be well past its designed service life but remains too costly to replace wholesale, predictive maintenance and the low cost sensor technology now available to support it represent a genuinely practical opportunity to reduce unplanned downtime.
Why Ageing Equipment Benefits Most
Predictive maintenance delivers the greatest value where failure risk is least predictable, and this is precisely the situation on ageing production equipment. A new machine, still within its original design life and maintained to specification, tends to fail in relatively predictable ways, making scheduled preventative maintenance reasonably effective. Equipment that has been in service for fifteen or twenty years, however, behaves less predictably. Bearings wear unevenly, motors draw increasing current as insulation degrades, and mechanical components develop faults that a fixed maintenance schedule was never designed to anticipate.
For manufacturers who cannot justify the capital cost of wholesale equipment replacement, and who are instead managing a mixed fleet of ageing and newer equipment, predictive maintenance offers a way to extend the useful life of older assets more safely, by giving genuine visibility into their actual condition rather than relying on generic maintenance intervals that may be too conservative in some cases and insufficient in others.
What IoT Sensor Technology Actually Involves
The technology underpinning modern predictive maintenance has become significantly more accessible in recent years. Low cost vibration sensors can be retrofitted to motors, pumps, and gearboxes to detect the early signs of bearing wear or misalignment, well before a fault becomes audible or produces a measurable temperature increase. Current sensors monitoring motor draw can flag developing electrical faults or increasing mechanical load. Temperature sensors on bearings, drives, and electrical cabinets provide early warning of overheating that often precedes failure.
Critically, this sensor data does not need to feed into an expensive, bespoke monitoring platform to be useful. Many modern industrial sensors can integrate directly with existing PLC infrastructure, feeding condition data into the same Allen Bradley or Siemens control systems already managing the production line, with alarms and trend data made visible through existing HMI screens rather than requiring an entirely separate monitoring system.
Retrofitting Sensors to Existing Control Systems
For manufacturers with existing Allen Bradley or Siemens PLC infrastructure, retrofitting condition monitoring sensors is often a more incremental and cost effective step than it initially appears. Rather than replacing an entire control system, sensors can frequently be integrated into spare input capacity on an existing PLC, or connected via a small additional input module, with new logic added to log trend data and raise alarms based on defined thresholds. This approach allows manufacturers to add predictive maintenance capability progressively, starting with the highest risk or highest consequence equipment, such as refrigeration compressors or critical line drive motors, rather than requiring a large upfront investment across the entire facility.
Building the Business Case
The business case for predictive maintenance rests on comparing the cost of sensor installation and monitoring against the cost of the unplanned downtime it prevents. For a critical piece of equipment, such as a refrigeration compressor supporting beer tank temperature control, or a drive motor on a bottling line’s primary conveyor, a single unplanned failure during a production shift can cost far more in lost production than several years of sensor monitoring on that equipment. This calculation becomes even more compelling when equipment failure carries downstream consequences, such as product spoilage from a refrigeration failure, or contamination risk from a failed hygiene critical system.
A realistic business case does not attempt to instrument every piece of equipment on a site simultaneously. It identifies the highest risk, highest consequence assets first, typically equipment that is both critical to production continuity and showing signs of age related unpredictability, and builds monitoring capability out from there based on demonstrated value.
Interpreting the Data: Where Experience Still Matters
Sensor data alone does not prevent breakdowns, it provides the information needed for an experienced engineer to make a maintenance decision. Distinguishing a genuine developing fault from normal equipment variation requires understanding of the specific equipment and process involved, and this is where the combination of sensor technology and genuine engineering experience becomes valuable. A vibration trend that would be alarming on one piece of equipment might be entirely normal on another, depending on its design, age, and duty cycle, and interpreting this correctly requires more than simply setting a generic alarm threshold.
Common Mistakes When Getting Started
Manufacturers new to predictive maintenance sometimes attempt to instrument too much equipment at once, purchasing sensors broadly across a facility without a clear plan for how the resulting data will actually be reviewed and acted upon. This tends to produce a large volume of trend data that nobody has the time or clear responsibility to properly monitor, undermining the value of the investment. A more effective approach starts narrow, with a small number of genuinely critical assets, and builds out review processes and alarm thresholds that are actually being used before expanding further.
Another common mistake is setting generic alarm thresholds without adjusting them to the specific equipment and its normal operating variation, leading either to alarm fatigue from excessive false positives, or missed genuine faults because thresholds were set too conservatively. Getting this right typically requires an initial period of baseline data collection on each piece of monitored equipment, understanding what normal variation actually looks like, before finalising alarm settings that will be genuinely useful rather than noise. Working with an engineering partner who already understands the specific equipment involved shortens this baselining period considerably, since prior experience with similar assets provides a reasonable starting point rather than beginning entirely from scratch.
A Practical Path Forward
For FMCG manufacturers running a mix of ageing and newer production equipment, predictive maintenance does not need to be an all or nothing, large capital investment. Starting with targeted sensor installation on the highest risk equipment, integrated into existing Allen Bradley or Siemens PLC infrastructure, offers a practical, incremental path toward better visibility of equipment condition and fewer unplanned breakdowns. BevTech’s combined electrical, automation, and mechanical engineering capability means this kind of project can be scoped and delivered as a single, coordinated piece of work, from sensor selection and installation through to control system integration and alarm logic. To discuss predictive maintenance options for your production equipment, contact BevTech at 25 Silvio St, Richlands QLD 4077, or admin@bevtech.com.au.