In industry, people often talk about “types of maintenance” as if one had to choose a single approach. In reality, it’s a continuum of complementary strategies that are combined according to equipment criticality, operational constraints, and the organization’s data maturity. The goal isn’t to follow a doctrine, but to achieve operational optimization: fewer unplanned downtimes, better-targeted interventions, enhanced safety, and ultimately, controlled total cost.
This article offers a clear and nuanced overview of existing approaches, provides a method for selecting the right strategy and avoiding common pitfalls, and explains how to move toward predictive maintenance even when historical failure data is scarce a very common operational reality.
This article provides a clear and nuanced overview of existing approaches, then offers a method for choosing a strategy, avoiding common mistakes, and explains how to transition to predictive maintenance when there is little historical data on failures, which is a very common operational reality.
The different industrial maintenance strategies.
Curative maintenance
Curative maintenance occurs after a failure. It is justified for non-critical assets whose breakdown does not pose safety risks or cause significant production losses. Typical examples include a workshop fan or a seldom-used backup pump. Its advantage lies in its simplicity. Its limitation is clear: no control over downtime, and costs can be high if the unavailable component halts an entire production line.
Corrective maintenance
Corrective maintenance takes place when an anomaly is detected, without a complete equipment shutdown. It involves correcting excessive play, replacing a leaking seal, or reconfiguring a drifting drive. It requires minimal monitoring and helps prevent major damage, but it remains reactive. For critical assets, it carries the risk of rapid deterioration if the anomaly is underestimated.
Systematic preventive maintenance (time-based or usage-based)
Here, interventions are scheduled according to a calendar (every six months) or usage (every 1,000 hours). The benefit is predictability: the workshop can organize itself, spare parts are available, and personnel are scheduled. The well-known limitation is over-maintenance: sometimes components are replaced too early, machines that are functioning properly are opened, and unnecessary downtime is caused.
Condition-based preventive maintenance
Condition-based maintenance relies on state measurements: vibration, temperature, pressure, current, acoustics, etc. As soon as indicators exceed thresholds, action is taken. This approach reduces over-maintenance by aligning interventions with the actual condition of the equipment. Its limitation lies in the precision of the thresholds and the quality of the signals. Thresholds that are too static may overlook slow drifts and miss the “small early warning signs” of a potential failure.
Prédictive maintenance
Predictive maintenance uses sensor time series to learn the normal behavior of equipment and detect significant changes early. An algorithm does not wait for a clear alarm; it identifies abnormal dynamics: a vibration appearing in a specific frequency band, an acoustic signature shifting, or a subtle variation in electrical consumption under load. The goal is not to predict the future to the exact minute, but to provide enough anticipation and explainability to intervene at the right moment, with the right parts, during the appropriate downtime window.
How to decide concretely? Asset criticality and data maturity
One can reason along two axes to choose a maintenance strategy:
- Criticality: What are the impacts of a failure (safety, environment, production, quality, costs)?
- Data maturity: What sensors are available, over what historical period, with what access and quality? Can the organization exploit this data (tools, skills, processes)?
With these two axes, the decision becomes logical. For a non-critical asset with little data, corrective or preventive maintenance will suffice. For a critical asset with limited data, systematic preventive maintenance is adopted to reduce risk, while monitoring a few key signals to transition to condition-based maintenance. Once data is available and usable, predictive maintenance becomes the most effective tool to reduce downtime and optimize planning.
Common pitfalls… and how to avoid them
The first pitfall is over-maintenance. Replacing components systematically “just to be safe” may provide short-term reassurance but creates artificial downtime and unnecessary parts and labor costs. The solution can be gradual: move from fixed rules to data-driven rules.
The second pitfall is less about using thresholds than about how they are defined. Setting a fixed threshold directly on a raw value for example, a bearing’s vibration level can miss a slow but significant drift. Conversely, well-constructed statistical thresholds can effectively detect anomalies when based on indicators derived from raw data: trend, noise level, average drift, etc.
The challenge lies in choosing these indicators and adjusting detection rules. This is precisely what machine learning enables: automating this step to identify the combinations of signals most representative of abnormal behavior.
The third, very common pitfall is reliance on historical failure data. Waiting years “to have enough cases” is economically nonsensical. The right approach is to learn the nominal behavior and detect significant deviations this is the essence of unsupervised methods.
What the predictive approach changes
The predictive approach transforms maintenance by shifting from reasoning based on average intervals to decisions based on the actual condition and dynamics of equipment.
Rather than waiting for a clear alert or a breakdown, normal operation is modeled using time series data from sensors (vibrations, temperatures, pressures, intensities, acoustics, etc.) and small but significant deviations that precede failure are identified. This paradigm shift has two key effects: on the one hand, a measurable gain in anticipation (days to weeks) to organize the intervention, order parts, and consolidate operations; on the other hand, greater explainability thanks to the identification of the variables that contribute most to the alert, which immediately guides the diagnosis.
In concrete terms, this means first leveraging existing data, covering typical operating conditions (start-ups, transients, load variations, seasonality), and establishing a continuous improvement loop between the field and the algorithm. The aim is not to predict a failure date down to the minute, but to reduce uncertainty: detect early, assess severity, and prioritize what matters for the safety, quality, and availability of the lines.
Integration with CMMS then allows alerts to be transformed into planned actions, with clear visibility of resources, required skills, and impacts on production.
Where systematic maintenance tends to create over-maintenance, predictive maintenance reallocates efforts: fewer unnecessary openings, fewer unjustified opportunistic shutdowns, and more targeted interventions at the right time.
It is particularly relevant when the history of breakdowns is limited a common situation for well-maintained critical assets because it learns nominal behavior rather than relying on a catalog of past failures. Ultimately, the organization gains operational peace of mind: fewer surprises, better-informed decisions, and smoother dialogue between maintenance, production, and quality.
A clear step-by-step transition to predictive analytics
It is possible to achieve tangible results in a matter of weeks, in short stages.
We start with an operational framework: identifying priority assets, feared failure modes, QHSE constraints, and possible shutdown windows. This is followed by an in-depth data diagnosis. This involves not only assessing its quality and frequency, but also verifying that it can effectively detect the targeted faults.
Two approaches are possible:
- Data approach: if a history of known failures is available, the models’ ability to identify them can be tested. If the detections work, the instrumentation is deemed sufficient. If not, this may reveal either inadequate instrumentation or faults that do not show any warning signs.
- “Physical” approach: in the absence of historical data, we start with the physical phenomena associated with each fault to check whether the measured variables (vibrations, temperatures, visual signals, etc.) are likely to reflect its occurrence.
Once this step has been validated, nominal modeling can be carried out on real data, covering different operating conditions (start-ups, transients, variable loads, seasonality). The models are then validated over periods of time kept separate from training before being put on alert: graduated notifications, prioritization by criticality, and integration with the CMMS to transform alerts into concrete actions (planning, ordering parts, mobilizing skills). Finally, a continuous improvement loop consolidates everything: field feedback, periodic recalibration, capitalization of diagnostics.
Conclusion
The five types of industrial maintenance are not mutually exclusive; they are tools that can be combined intelligently. The key is to align criticality, data reality, and processes to intervene at the right time. The strength of time series-based predictive analytics is that it offers anticipation with explanation, even when there is little historical failure data.
Want to quickly assess the potential of predictive analytics for your critical assets? Tell us about your sensor data and operating environment. We will propose a concrete trial plan tailored to your constraints.
