Background Information

Different condition monitoring approaches can be taken to meet the objective of improving operational efficiency by reducing maintenance costs, minimising downtime (increase availability), improving reliability and optimising performance.

Condition Monitoring Approaches:

Maintenance Approach


Key Action/Output


Effectively a ‘run to failure’ strategy, allowing 24/7 operation with no interruptions for maintenance. This can be appropriate where asset reliability is high and failure impact is low – typically when replacement/repair cost is low or where back-up assets are readily available.

“It’s broken, I need to fix it now”

Planned /
Schedule based

A proactive approach of preventative maintenance, reliant on regular/periodic maintenance work at intervals based on the usage, criticality and value of the asset. The schedule is typically driven by a time-based schedule (e.g. running hours) or a usage-based schedule (e.g. miles driven). The schedule is usually created based on data from Reliability Centered Maintenance policy, supported by a Failure Mode Effects and Criticality Analysis (FMECA). The scheduled maintenance approach can result in parts being replaced or retired long before they are actually worn out.

“Can I keep it running longer”?

Condition based

Condition-based maintenance (CBM) is focused on detecting and diagnosing faults earlier to allow pre-emptive maintenance, based on evidence that the machine performance is deteriorating and is likely to fail in the future. Various machine parameters are monitored and used to derive condition indicators which are influenced by the health of the machine over time. Unusual behaviour patterns (anomaly detection) can be detected and used to instigate appropriate remedial action. Simple trend analytics can be applied and used to extend time between planned maintenance activities, based on the known health of the machine at a given time.

“What is my problem?”


Adds failure knowledge, risk assessment and time forecasting to the diagnostic function to estimate Remaining Useful Life (RUL) enabling a prognostic assessment to be made.
Correlating machine performance with known failure characteristics enables predictions of the time to the next failure to be estimated (data driven machine learning). The output is a prognostic forecast based on knowledge, risk and probabilities.

“How long have I got?”


Optimizes RUL by integrating the CM with the maintenance planning system to get the corrective action resources in place at the right time.
Prescriptive Maintenance does not only identify a problem or predict a failure, it provides a focused solution that can be acted upon by generating advisory information.

“What should I do?”



Definitions (ref. Wikipedia)

Preventive maintenance (PM)

PM is “a routine for periodically inspecting” with the goal of “noticing small problems and fixing them before major ones develop.” Ideally, “nothing breaks down.”

Condition Monitoring (CM)

CM is the process of monitoring a parameter of condition in machinery (vibration, temperature etc.), in order to identify a significant change which is indicative of a developing fault. It is a major component of predictive maintenance.

Condition Based Maintenance (CBM)

CBM is a maintenance strategy that monitors the actual condition of the asset to decide what maintenance needs to be done. CBM dictates that maintenance should only be performed when certain indicators show signs of decreasing performance or upcoming failure.

Predictive Maintenance (PdM)

PdM techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted.

Reliability-centered maintenance (RCM)

RCM is a concept of maintenance planning to ensure that systems continue to do what their user require in their present operating context. Successful implementation of RCM will lead to increase in cost effectiveness, reliability, machine uptime, and a greater understanding of the level of risk that the organization is managing.

Prescriptive analytics

Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen.