An automatic method to schedule maintenance and repair of complex systems is produced based on a computational structure called the Informed Maintenance Grid (IMG). This method provides solutions to the two fundamental problems in autonomic logistics: (1) unambiguous detection of deterioration or impending loss of function and (2) determination of the time remaining to perform maintenance or other corrective action based upon information from the system. The IMG provides a health determination over the medium-to-long-term operation of the system, from one or more days to years of study. The IMG is especially applicable to spacecraft and both piloted and autonomous aircraft, or industrial control processes.
Condition-Based Maintenance (CBM) has become popular for complex systems due to its cost and reliability advantages over traditional scheduled maintenance programs. However, CBM is frequently difficult to apply owing to system complexity and the highly stochastic nature of system use and environmental effects. A scalable solution capable of providing a substantial look-ahead capability is required. The IMG method was developed to satisfy this need.
The IMG is based upon a three-dimensional projection, relating successive computations of cross-signal features. The two short axes represent different sensed parameters from the system (typically performance parameters such as temperatures, pressures, etc.), with each pixel representing the coherency between measurements. The third axis represents time, displaying the progression of abnormalities as the system is used.
The IMG is a component of the larger BEAM system ("Beacon-Based Exception Analysis for Multimissions" [NPO-20827], NASA Tech Briefs, Vol. 26, No. 9 (September 2002), page 32). The coherence calculation used in the IMG is derived from the Information State Estimator (ISE) component of BEAM. The ISE computes relationships between large and diverse classes of signals and compares them to an internal statistical model for the purpose of anomaly detection. This notion is extended by the IMG, which combines and normalizes numerous results while providing an operational
Graphically, the IMG is represented (see figure) as a color-coded temporal succession of two-dimensional plots, each representing the coherence divergence from the statistical model. From this graphical object, one can easily discern the true functional operability of the system, detect the presence and impact of faults or persistent degradation, and assess the effectiveness of repairs or configurational changes. Maintenance recommendations can be derived automatically from this object, providing a continuous evaluation of the need for condition-based maintenance.
The following list outlines the necessary construction steps to apply the IMG:
- Provide examples of nominal data and partial physics models where possible for purposes of ISE training,
- Obtain example data of degraded or anomalous performance for training purposes,
- Compose a listing of preferred maintenance actions to correct faults in particular components, and
- Provide a mapping between sensed or manually supplied status variables and system operating mode.
Acceptable operating limits must be established in order to tune prognostic performance for cost effectiveness. These limits must either be set by system experts or "learned" as degradations appear in practice. Like the ISE itself, the IMG is easily upgraded once additional information is available. Limits may also be set using the same thresholds chosen for fault protection.
This work was done by Sandeep Gulati and Ryan Mackey of Caltech for NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.techbriefs.com/tsp under the Information Sciences category.
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Refer to NPO-20831, volume and number of this NASA Tech Briefs issue, and the page number.
This Brief includes a Technical Support Package (TSP).
Prognostics Methodolohy for Complex Systems
(reference NPO-20831) is currently available for download from the TSP library.
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