Industrial maintenance is a critical pillar of operational performance. In Industry 4.0 environments, where production systems are increasingly automated and interconnected, ensuring equipment reliability is more strategic than ever.
There are three main types of industrial maintenance used across production sites: corrective, preventive and predictive maintenance. Each approach reflects a different level of maturity in maintenance strategy. Let us examine them in detail.
Corrective or breakdown maintenance
Corrective maintenance, also known as breakdown maintenance, consists of repairing equipment after a failure has occurred.
Its objective is simple: restore the asset to its original operating condition as quickly as possible.
Corrective maintenance can involve:
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Repairing a complete failure that stops the machine
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Addressing a partial malfunction affecting performance or quality
While unavoidable in certain situations, this approach has significant drawbacks:
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Unplanned downtime
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Production losses
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Emergency interventions
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Potential safety risks
Because it is reactive by nature, corrective maintenance is often considered the least mature strategy. However, it remains necessary when no anticipation mechanism is in place.
Preventive maintenance
Preventive maintenance aims to reduce the probability of failure by performing maintenance tasks at predefined intervals.
These intervals may be based on:
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Time
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Usage cycles
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Manufacturer recommendations
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Regulatory requirements
Preventive maintenance helps preserve equipment and ensures compliance with safety standards. It is widely used across industrial sectors.
However, it presents structural limitations.
Preventive schedules are based on theoretical wear assumptions, not on the real condition of assets. As a result:
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Components may be replaced too early
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Resources may be mobilised unnecessarily
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Unexpected failures may still occur
Preventive maintenance represents a step forward compared to corrective maintenance, but it does not fully eliminate operational uncertainty.
Predictive maintenance
Predictive maintenance marks a shift from time-based to condition-based maintenance.
It relies on real operational data collected from sensors and industrial systems. By applying advanced analytics and machine learning algorithms, predictive maintenance detects early signs of abnormal behaviour and anticipates failures before they occur.
This approach enables:
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Reduced unplanned downtime
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Optimised maintenance scheduling
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Better spare parts management
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Improved asset lifetime
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Enhanced operational reliability
Unlike preventive maintenance, predictive strategies adapt to the real condition of equipment rather than theoretical assumptions.
The Role of Artificial Intelligence in Predictive Maintenance
With Industry 4.0, predictive maintenance has evolved significantly thanks to artificial intelligence.
Amiral Technologies has developed DiagFit, a predictive maintenance software designed to help industrial experts detect anomalies and accelerate diagnosis across complex multi-equipment environments.
DiagFit:
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Learns normal equipment behaviour
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Detects deviations automatically
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Allows experts to validate or reject alerts
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Continuously improves model accuracy
The solution works with heterogeneous industrial data and integrates with CMMS systems, helping bridge the gap between operational data and maintenance decision-making.
Predictive maintenance therefore represents not just a technical evolution, but a strategic transformation of maintenance management.
Conclusion
The three types of industrial maintenance reflect different levels of operational maturity:
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Corrective maintenance reacts
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Preventive maintenance schedules
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Predictive maintenance anticipates
As industrial environments become more complex and performance requirements increase, organisations are progressively moving toward predictive and data-driven maintenance strategies.
The challenge today is no longer simply repairing equipment, but anticipating failures and guiding maintenance decisions with actionable insights.
