How to achieve an efficient maintenance strategy

Corrective maintenance (often referred to as reactive maintenance) is carried out after a failure has been detected. Usually this is a reactive approach for when your machinery has already failed, you will carry out corrective maintenance to repair, restore or replace your machine back to its operational state.

This reactive action is performed when a problem has already occurred, during an emergency repair, or during a routine maintenance period. It is the most basic form of maintenance strategy, requiring little to no advance planning or management overhead.

Whilst corrective maintenance means minimal planning and lower immediate operational costs, it does expose organizations to extensive downtime, unpredictable fluctuations in output, as well as higher long-term running costs.

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Preventative maintenance aims to maintain the machine in accordance with a prescribed time schedule to reduce the probability of failure. Machinery manufacturers will often provide specific Mean Time Between Failure (MTBF) information, identifying the average length of time between failures of a machine and its components. Alternately a suitable maintenance schedule, using a locally generated MTBF, may be derived by analyzing historic maintenance events and records.

Planned maintenance activities are scheduled for when the MTBF number is approaching or has been exceeded. At this point, machinery is inspected, serviced, repaired, restored, or has components replaced on a calendar basis or as inspection warrants.

The saying goes “over-maintenance is cheaper than failure”, in theory preventative maintenance can reduce the amount of unplanned downtime and reduce operational risk since components should always be in good condition. However, it does not consider actual usage of the machine and cannot indicate potential failure points, often causing more downtime due to both planned and unplanned maintenance.

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Condition-based maintenance (CBM) relies on using sensing equipment to monitor physical parameters that relate to the actual condition of a machine. These are the vital signs of machine health, like vibration, current and acoustic signature. This data can be offline (handheld device inspections) or online (real-time data collection) and once collected is interpreted by expert human operators, often assisted by data analytics systems, to help understand when to schedule maintenance time.

CBM allows organizations to have deeper information on the current condition of the machinery on which their business depends. This allows experienced maintainers to diagnose machinery issues more precisely and start to plan as to when the optimum time for maintenance may be.

Condition based maintenance comes with the human burden of data collection and analysis. It often requires a high level of knowledge and training and the number of machines that can be monitored can be limited by the availability of specialist resource.

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Predictive Maintenance uses Artificial Intelligence (AI) to automatically interpret condition monitoring data to understand current machine condition and forecast future condition and timeframes. Some solutions can also prescribe relevant maintenance actions. This liberates maintenance and operations teams to plan much more effectively and monitor more machines.

The technology that drives predictive maintenance provides a safety-net regardless of other maintenance strategies and maturity. It provides advanced insights that are easily understood and actionable and does not require maintainers to spend their time analyzing raw condition information. It is particularly useful to reduce management overhead and allow efficient forward planning and resource use.

Predictive and prescriptive maintenance require suitable machine condition data which can come from investment in Industry 4.0 / Industrial IoT technologies. It’s a more efficient approach than other methodologies as maintenance can be planned to happen before any significant damage occurs and is based on the real condition of that machine, taking into account its usage cycle and not just its operating time.