Predictive Maintenance — definition
Predictive maintenance is a maintenance strategy that uses condition data, such as vibration, temperature or current signatures, to estimate equipment health and schedule maintenance before failure occurs.
It typically relies on sensors and analytics to detect early degradation patterns, allowing maintenance to be scheduled based on actual condition rather than fixed time intervals. Predictive maintenance sits alongside preventive maintenance (time-based) and reactive maintenance (after failure) as one of the main maintenance strategies.
Why it matters to industrial buyers
Shifting from time-based to condition-based maintenance can reduce unplanned downtime and unnecessary servicing, but requires investment in sensors, data infrastructure and analysis capability.
Key reference points
Common sensor types
Vibration, thermal imaging and motor current signature analysis are common data sources for predictive maintenance.
Data dependency
Reliable predictive maintenance commonly requires a period of baseline data collection before failure patterns can be identified with confidence.
Commonly confused with
preventive maintenance
Preventive maintenance follows fixed time or usage intervals regardless of actual condition; predictive maintenance triggers action based on measured equipment condition.
How it is used in practice
Vibration monitoring on a critical gearbox flagged an early bearing wear pattern, allowing planned replacement before an unplanned failure.
Frequently asked questions
Is predictive maintenance suitable for all equipment?
It is generally most cost-effective for critical or high-value assets where downtime cost justifies the sensor and analytics investment.
Does predictive maintenance eliminate unplanned downtime entirely?
No, it reduces but does not eliminate unplanned failures, since not all failure modes are detectable in advance.
Go deeper on the platform
Related terms
Industrial Internet of Things (IIoT)
Industrial Internet of Things (IIoT) refers to networks of connected sensors, devices and machines that collect and exchange operational data to support monitoring, analytics and automation across a facility.
Digital Twin
Digital twin is a virtual model of a physical machine, line or facility that is kept synchronised with real operational data to support simulation, monitoring or predictive analysis.
Overall Equipment Effectiveness (OEE)
Composite performance metric combining availability, performance and quality rates to express how effectively a production asset converts planned time into good output.
Retrofit Engineering
Retrofit engineering is the design and installation of upgraded components, controls or subsystems into existing machinery to extend service life, improve performance or meet updated standards.
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Reference content only. Global B2B Group is independent of equipment manufacturers and financing institutions; definitions are provided for education and do not constitute engineering, financial or legal advice.