30 HYDRAULICS & PNEUMATICS September 2026 www.hpmag.co.uk Overall Equipment Effectiveness (OEE) has proved useful because it brings three familiar sources of production loss into a common measure. Availability reflects the proportion of planned production time during which equipment is actually running, performance accounts for losses associated with running below the ideal production rate, including slow cycles and small stops, and quality reflects the proportion of output produced correctly first time. Used properly, OEE therefore provides production and engineering teams with a consistent way of identifying where productive capacity is being lost, although the headline percentage is considerably less informative than the losses from which it is calculated. One limitation is that OEE principally describes production performance rather than equipment health. By the time a fourhour breakdown appears as an availability loss, the maintenance department is already well aware that the machine has stopped, while repeated minor stops or a gradual reduction in production rate may have affected output for several shifts before the accumulated performance loss attracts attention. Real-time OEE reporting shortens the delay in seeing these effects, but it does not by itself establish the mechanical, electrical, fluid-power or process condition responsible for them. Condition monitoring addresses a different part of the problem and has been doing so since long before the current interest in artificial intelligence. Vibration analysis, oil analysis, thermography, electrical measurements and process parameters can all provide evidence of developing faults, while established diagnostic and prognostic techniques can help engineers assess deterioration and, where sufficient information exists, estimate future asset condition. These approaches can be relevant across a wide range of production assets, including machinery incorporating hydraulic and pneumatic systems. Predictive maintenance should not therefore be regarded as synonymous with AI, since the principle of using information about asset condition to inform maintenance decisions is already well established. What has changed is the quantity and variety of information that can economically be collected from production equipment and the ability to analyse relationships within those data. PLCs, drives, machine controllers, condition monitoring systems, vision equipment and additional sensors can provide information about an asset while it is operating, allowing production behaviour to be examined alongside indicators of mechanical or process condition. On machinery incorporating fluid-power systems, relevant operating information may also include measured pressure, temperature or other process parameters where these are already available. A motor drawing progressively more current, for example, may be unremarkable in isolation, as might a small change in cycle-time consistency, but a relationship between several such changes may warrant investigation even though none has reached an established alarm level. Looking beneath the OEE figure Machine learning can be useful where relevant behaviour is represented by relationships between several variables or where normal operating conditions vary according to product, speed, load or process state. This does not make established condition monitoring obsolete, nor does it mean that an algorithm will necessarily identify a developing fault more reliably than an experienced analyst using an appropriate technique. Its potential lies in examining larger and more complex sets of operating data than would normally be practical for a person to monitor Looking beyond OEE to understand production losses Overall Equipment Effectiveness (OEE) has traditionally helped manufacturers quantify where productive capacity is being lost, but the growing availability of machine, process and condition data is creating an opportunity to recognise some of the conditions associated with those losses earlier. For plants operating hydraulic and pneumatic equipment, that information can include pressure, temperature and other relevant operating and condition data. Artificial intelligence may extend that capability, provided it is applied to an engineering problem rather than treated as an end in itself. H&P reports. SPECIAL REPORTS – CONDITION MONITORING
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