Small Business Innovation Research/Small Business Tech Transfer
Diagnosis-Driven Prognosis for Decision Making
Project Description
One cannot build a system-level Prognosis and Health Management (PHM) solution by cobbling together a bunch of existing prognostic techniques; it will have a very high rate of false-positive indications. On the other hand, if a system-level health management solution could identify the individual degradations and indictors associated with those degradations, and thereby decouple the problem into smaller pieces, the existing prognostic techniques could still be used to predict time to failure, and could therefore drive an effective Condition Based Maintenance and Decision Support System (CBM+). Qualtech Systems, Inc. (QSI) and Vanderbilt University team seeks to develop a system-level diagnostic and prognostic process and a "sense and respond capability" which first uses error codes and discrete sensor values to correctly diagnose the system health including degradations and failures of sensors and components, and then invoke appropriate prognostic routines for assessment of remaining life and capability. Thus, QSI's Testability Engineering And Maintenance System (TEAMS) real-time reasoner will enable the use of many existing prognostics techniques in the broader context by decomposing the complex system into local datasets of degradations and associated sensor data sets, thereby limiting the problem-space for the prognostic techniques to their limited design scope. Indeed, it is well established in the contexts of parameter estimation and model-based fault identification (i.e., fault isolation and severity estimation) that feature selection and diagnosis, respectively, followed by parameter estimation provides major improvements in estimation performance (measured in terms of computational time as well as the standard deviations of the estimated parameters) when compared to full parameter estimation which provides biased estimates for all the parameters.
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Anticipated Benefits
NASA is developing increasingly autonomous systems that can perform missions with a high degree of certainty with minimal human intervention. Examples of such mission include rovers operating in Mars, where the missions are extremely long, and therefore multiple components and subsystems will degrade and fail over the duration of the mission. However, due to the long communication delays between Mars and Earth, these systems can be monitored and diagnosed by mission control like any other near-earth mission. The proposed capability will be invaluable to NASA for such operations by (a) Predicting failures before they disrupt the mission, (b) Reducing false positives of such prediction with the proposed diagnosis-driven prognosis, and (c) identifying the remaining useful capability of the system. This will enable NASA to focus on the mission planning and recovery aspects, and manage the health of the system, rather than being blindsided by unexpected failures.
The potential applications for DoD and Commercial users are even larger. This is because they are likely to operate multiple systems, a fleet of vehicles for example, that have the opportunity of periodic preemptive maintenance. To address these customers' needs, we will develop a decision-support module on top of the proposed capability here that will allow the customer to define his own business rules. Such business rules will help the customer answer questions like "if the system has a scheduled downtime window of 2 hours tomorrow, what pre-emptive repairs should I perform within that maintenance window so as to minimize the chance of unscheduled downtime (due to failure) in the next 10 days". For enterprise-wide logistic planning, this decision-making capability will also help optimize the cost of additional opportunistic maintenance versus the cost of additional downtime if such maintenance were not performed. The capability developed here is key to proving the business case for prognostics in commercial and military applications. More »
The potential applications for DoD and Commercial users are even larger. This is because they are likely to operate multiple systems, a fleet of vehicles for example, that have the opportunity of periodic preemptive maintenance. To address these customers' needs, we will develop a decision-support module on top of the proposed capability here that will allow the customer to define his own business rules. Such business rules will help the customer answer questions like "if the system has a scheduled downtime window of 2 hours tomorrow, what pre-emptive repairs should I perform within that maintenance window so as to minimize the chance of unscheduled downtime (due to failure) in the next 10 days". For enterprise-wide logistic planning, this decision-making capability will also help optimize the cost of additional opportunistic maintenance versus the cost of additional downtime if such maintenance were not performed. The capability developed here is key to proving the business case for prognostics in commercial and military applications. More »
Project Library
Primary U.S. Work Locations and Key Partners
| Organizations Performing Work | Role | Type | Location |
|---|---|---|---|
| Qualtech Systems, Inc. | Lead Organization |
Industry
Minority-Owned Business,
Small Disadvantaged Business (SDB)
|
Rocky Hill, Connecticut |
Ames Research Center
(ARC)
|
Supporting Organization | NASA Center | Moffett Field, California |
Primary U.S. Work Locations
-
California
-
Connecticut
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