Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function.
The predicted time then becomes the remaining useful life (RUL), which is an important concept in decision making for contingency mitigation.
Prognostics predicts the future performance of a component by assessing the extent of deviation or degradation of a system from its expected normal operating conditions.
[2] The science of prognostics is based on the analysis of failure modes, detection of early signs of wear and aging, and fault conditions.
Therefore, domain knowledge and statistical signal processing is applied to extract important features from (more often than not) noisy, high-dimensional data.
Since measurements of critical damage properties (such as stress or strain of a mechanical component) are rarely available, sensed system parameters have to be used to infer the stress/strain values.
Micro-level models need to account in the uncertainty management the assumptions and simplifications, which may pose significant limitations of that approach.
[citation needed] Another way to accomplish the pre-estimate aggregation is by a combined off-line process and on-line process: In the off-line mode, one can use a physics-based simulation model to understand the relationships of sensor response to fault state; In the on-line mode, one can use data to identify current damage state, then track the data to characterize damage propagation, and finally apply an individualized data-driven propagation model for remaining life prediction.
Then, they used a particle filter approach to derive the dynamic form of the degradation model and estimate the current state of capacitor health.
Those metrics were primarily accuracy and precision based where performance is evaluated against end of life, typically known a priori in an offline setting.
This is important because prognostics is a dynamic process where predictions get updated with an appropriate frequency as more observation data become available from an operational system.
As a system approaches failure, the time window to take a corrective action gets shorter and consequently the accuracy of predictions becomes more critical for decision making.
Finally, randomness and noise in the process, measurements, and prediction models are unavoidable and hence prognostics inevitably involves uncertainty in its estimates.
[15][16][17][18][19] For most PHM industrial applications, commercial off the shelf data acquisition hardware and sensors are normally the most practical and common.
There are numerous sensor vendors for those measurement types, with some having a specific product line that is more suited for condition monitoring and PHM applications.
National Instruments currently has a trial version (with a commercial release in the upcoming year) of the Watchdog Agent prognostic toolkit, which is a collection of data-driven PHM algorithms that were developed by the Center for Intelligent Maintenance Systems.
Customized predictive monitoring commercial solutions using the Watchdog Agent toolkit are now being offered by a recent start-up company called Predictronics Corporation[23] in which the founders were instrumental in the development and application of this PHM technology at the Center for Intelligent Maintenance Systems.
Another example is MATLAB and its Predictive Maintenance Toolbox[24] which provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis.
This toolbox also includes reference examples for motors, gearboxes, batteries, and other machines that can be reused for developing custom predictive maintenance and condition monitoring algorithms.