Predictive maintenance is a maintenance strategy based on the systematic assessment of an asset’s condition and performance to anticipate the need for intervention before a functional failure occurs. Instead of performing a task merely because a predefined time interval has elapsed, the decision is supported by actual signs of degradation, trends, measurements, inspections, and technical analysis.<\/p>\n\n\n\n
In the classical terminology of ABNT NBR 5462, predictive maintenance uses systematic analysis techniques and monitoring or sampling methods to reduce unnecessary preventive interventions and decrease corrective maintenance. The contemporary asset-management approach broadens this reasoning: ABNT NBR ISO 55001:2024 treats predictive action as decision support for determining the appropriate time for maintenance, renewal, replacement, or decommissioning, relating condition, performance, risks, opportunities, and costs.<\/p>\n\n\n\n
Therefore, predictive maintenance does not simply mean installing sensors or performing thermography. A program is technically consistent only when it transforms reliable data into diagnosis, criticality assessment, and engineering decisions. For some assets, the best decision will be to continue operating and monitor; for others, to bring an intervention forward; in critical situations, to develop an upgrade design, review protection systems, plan a shutdown, replace an asset, or change the maintenance strategy itself.<\/p>\n\n\n\n
It is especially useful when there is a degradation mechanism that can be detected before loss of the required function and when the interval between anomaly detection and failure is long enough to analyze, plan, and execute a response consistent with the risk. When failure is essentially random, no technically correlated condition variable exists, or monitoring cost is not justified, other strategies may be more appropriate.<\/p>\n\n\n\n\n
The logic begins with the function the asset needs to perform and the failure modes capable of compromising that function. Only then are the parameters that can actually reveal degradation defined. Temperature, vibration, insulation resistance, partial discharge, oil quality, ultrasound, electrical current, pressure, flow, or other variables have no value by themselves: they become useful when they have a known relationship with a failure mechanism and are analyzed under actual operating conditions.<\/p>\n\n\n\n
An isolated measurement is rarely sufficient. The result needs to be compared with acceptance criteria, history, trend, load condition, environment, asset characteristics, and, where applicable, manufacturer references and design requirements. This contextualization is what allows a normal process variation to be distinguished from degradation that requires investigation.<\/p>\n\n\n\n