1.
What is Preventive Maintenance?
1.1.
Preventive Maintenance vs Predictive Maintenance: What Are the Differences?
2.
Types of Preventive Maintenance
2.1.
Time-Based Maintenance
2.2.
Usage-Based Maintenance
2.3.
Condition-Based Maintenance (CBM)
2.4.
Predictive Maintenance
3.
The Future of Preventive Maintenance
‘Better safe than sorry’ is a common phrase among operations and maintenance (O&M) teams. An overlooked deviation in the operating temperatures may lead to an electrical component failure. Undetected corrosion under insulation—to pipeline rupture and environmental pollution.
The consequences of poor asset maintenance are in the best case very expensive and in the worst—mortally dangerous for staff. So naturally, most companies attempt to apply preventive maintenance to a larger proportion of their asset portfolio.
Preventive maintenance (PM) assumes regular, scheduled corrective activities aimed at preserving asset integrity and minimizing failure risks. By performing regular inspections, servicing, and asset conditioning actions, O&M teams stay ahead of the curve rather than waiting until a problem becomes apparent.
Reactive maintenance leads to prolonged downtime, which has a ripple effect further down the supply chain—delays in manufacturing, resource waste, quality control issues, inventory size shrinkage, lost sales, and ultimately customer frustrations. An unplanned shutdown of Neste’s Singapore biorefinery in October 2024 had a ripple effect on the company’s US sales until the end of the year, leading to lost sales as the US blender tax credit was about to expire.
Of course, not every problem can be fully mitigated, especially when you lack data about the degradation. But many operational issues can be detected early with a preventive maintenance schedule.
The majority of business leaders 87% state preventive maintenance reduces machine breakdowns and lost production time. Companies that primarily rely on preventive and predictive maintenance approaches experience 15 % less downtime, have an 87 % lower defect rate, and 66 % less inventory increases due to unplanned maintenance, according to a NIST study. Moreover, they also have faster lead- and production times due to minimized downtime, enabling greater agility in response to fluctuations in supply and demand.
Preventive maintenance (PM) is often mentioned alongside predictive maintenance (PdM). Both have a shared aim—asset failure prevention, but the means to reaching that goal are different.
Preventive maintenance strategies are schedule-driven. Maintenance activities are carried out at predetermined intervals (e.g., weekly, monthly, quarterly, etc). The frequency is established based on applicable compliance requirements, industry testing standards, and OEM guidelines when applicable. In many cases, routine maintenance is carried out regardless of the asset’s actual condition, sometimes leading to over- or under-serving.
Predictive maintenance strategies, in contrast, are condition-based. Maintenance needs are determined using the latest data on the asset’s health scores, created based on data from sensors, IoT devices, inspection drones, and other asset management systems. Corrective maintenance is only carried out if the PdM system suggests a strong propensity for performance degradation or failure.
Effectively, predictive maintenance takes prevention a notch further, providing asset management teams with multi-dimensional, real-time insights on equipment degradation. For example, changes in acoustic emission levels, operating temperatures, or wall thickness can
indicate problems at the onset. Yet, traditional asset monitoring solutions aren’t often tuned to pick out these signals.
Predictive maintenance solutions supply richer insights into asset conditions by aggregating condition data from multiple sources and analyzing it with machine learning or deep learning algorithms. When connected to an asset management system, predictive algorithms can provide insights into future asset performance, issue alerts about performance degradation, and provide accurate remaining useful life (RUL) estimates.
Understandably, predictive maintenance strategies are more expensive to implement as they require new technology investments and process changes. However, in the long run, a preventive maintenance strategy brings dividends in the form of lower production losses and minimal unplanned downtime.
Preventive maintenance is an overarching term for four different approaches for averting sudden asset failures and subsequent unplanned downtime. These include time-based maintenance, usage-based maintenance, condition-based maintenance, and predictive maintenance.
As it says on the tin, time-based maintenance assumes scheduling maintenance activities at fixed intervals — daily, weekly, quarterly, bi-annually, etc. The frequency depends on the type of asset, its risk scores, operating conditions, and applicable industry regulations.
For example, thorough internal inspections of gasoline storage tanks must be carried out every 5 years. E and F Class combustion turbine rotors at power plans must undergo requalification inspection after 2,000 to 5,000 operating hours to determine the remaining operating hours. Many OEM warranty and support policies also establish specific timeframes for carrying out inspections and maintenance.
The advantage of a time-based maintenance strategy is the ease of scheduling and work order management. This approach often results in under- or over-servicing since actual wear and tear isn’t taken into account.
Time-based maintenance approach also results in a larger number of work orders, requiring bigger teams. Given that half of maintenance teams struggle with hiring new talent, this strategy can create extra pressure for the teams, leading to oversight.
Usage-based maintenance (UBM) strategy aligns the servicing schedule with actual asset usage. It can be driven by metrics like operating hours, mileage, load levels, start/stop cycles, or vibration levels.
Modern computerized maintenance management systems (CMMS) provide facility managers with data-driven lifecycle insights, based on asset age, usage rates, and past history of maintenance. They can also provide recommendations for capital asset replacement to better inform budgets and coordinate capital replacement schedules.
Centrax, an OEM of high-efficiency power generation packages of gas turbines, relies on a preventive maintenance solution to monitor over 200 deployed assets. By monitoring engine data, Centrax can tailor inspection schedules based on customer needs. Real-time data also provides better insights for new product design and development.
In the chemical industry, UBM strategies can be applied to critical assets like centrifuges, compressors, pumps, and reactor agitators among others. By improving their asset management approaches, chemical companies can improve their overall equipment effectiveness (OEE) by 8% to 30%, according to Michiel van den Boomen of EFESO.
Condition-based maintenance (CBM) informs asset servicing with real-time data on its performance. When tracked parameters dip below an acceptable threshold, indicating deterioration or impending failure, maintenance is administered. For example, if a sensor detects abnormal vibration levels in drilling equipment, maintenance is scheduled to run a diagnostic check, lubricate the moving parts, and recalibrate the equipment.
Norwegian Equinor recently adopted a condition-based maintenance strategy for 40% of its equipment. Powered by SAP technology, the system integrates sensor data from Equinor’s field systems through an IoT gateway. Users then set custom rules to trigger notifications (e.g., a rise in vibration levels above an acceptable threshold). The notifications are synchronized with the backend asset management system, eliminating manual data entry. Equinor teams now have real-time data on asset health status, ranging from “excellent” to “unacceptable” for streamlined decision-making. Alerts provide extra context on the detected issue and guidance on the next best steps.
CBM is a reliable strategy for assets with predictable degradation patterns and non-ambiguous indicators that indicate pending issues. It also makes sense for assets, where inspectors could confirm the problem, detected by internal sensors, using anothernon-invasive methods like visual inspection with a camera drone or an inspection drone with an ultrasonic transducer like Voliro T.
Predictive maintenance strategy combines real-time monitoring with advanced analytics methods for forecasting asset performance degradation and failure. While condition-based monitoring alerts about already present issues, PdM tools approximate when problems may arise in the future giving O&M teams extra time to react.
Advanced PdM models can predict with high accuracy the remaining wind turbine blade lifetime or anticipate different component failures in aircraft. Shell recently rolled out a predictive maintenance solution for control valves at one of its refineries in the Netherlands. The algorithm was trained on historical asset data to recognize anomalies in performance. Whenever any deviation is detected, the on-site team receives an alert. Since launch, the system detected problems in 65 control valves, which have been undetected by traditional inspection methods. Early detection meant that valve maintenance could be proactively planned at the best time, rather than applied reactively, resulting in higher downtime and operational costs.
A PdM strategy enables ‘just in time’ servicing, helping businesses avoid crippling downtime. However, such solutions also require substantial investments in new digital technologies and process-related changes. Moreover, you’ll need access to sufficient data for model training and validation (which may be in short supply for certain asset types).
Generally, predictive maintenance makes sense when:
Hands down, preventive maintenance is a better approach than reactive maintenance. But PM also has high upfront investment costs in new equipment, hardware, software, staff training, and process optimization. Limited data collection either due to challenging acquisition or system interoperability issues, further slows down the adoption process.
Even when all the technical components are in place, companies face limitations in human resources. Almost three-quarters of leaders surveyed by McKinsey said they struggled with recruiting new maintenance technicians. Subsequently, preventive maintenance makes up only 51% of all carried-out maintenance activities.
Clearly, O&M teams need an efficiency boost for their asset management program, and new preventive maintenance technologies promise to deliver just that.
Affordable Internet of Things (IoT) devices enable real-time monitoring over a wide range of assets, unlike SCADA’s periodic or event-based data collection, leading to delayed responses. IoT sensors can also capture a wider range of parameters—vibration, temperature, moisture levels, and acoustic waves. When connected to a secure cloud-based data processing network, IoT networks also provide remote visibility and remote control, unlike SCADA’s typically localized control.
Specialized industrial inspection drones also enable faster and more frequent inspections of large-scale assets. To collect thickness measurements of a storage tank or inspect for early signs of corrosion in elevated piping, you no longer need to construct scaffolding or call in a team of rope climbers. Instead, you can use Voliro T.
Equipped with six interchangeable payloads for non-destructive testing at heights, Voliro T can approach assets from every angle to collect reliable readings in twice less time than using conventional methods. With our technology, inspection crews can complete full flare stack inspection in just 2 hours or complete 5 to 10 industrial storage tank inspections per day.
Easier, less cumbersome conditioning data collection gives you extra visibility into the current asset health and helps you build a reliable historical database for training predictive maintenance models.
