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Systems Health Management

Systems Health Management plays a vital role ensuring the cost effective and safe operation of aerospace systems. It includes techniques for the detection, diagnosis and prognosis of degradation or faults in a vehicle system or its component. Additionally, it provides a recommended response to the degradation or fault such as a recommended maintenance action (off-line) or reconfiguration for fault mitigation (on-line).

The broad benefits of systems health management include:

At NASA Glenn, the Systems Health Management sub-discipline has two primary technology focus areas.

Aircraft Engine Gas Path Health Management

Gas path health management is a cornerstone capability for monitoring the health of aircraft gas turbine engines. Its founding principles are based upon the parameter interrelationships inherent within a gas turbine engine cycle. Through the analysis of engine sensor measurements collected over time, gas path health management enables the estimation and trending of performance deterioration occurring within the major modules of the engine as well as the diagnosis of system faults.

Health management flow chart
Gas path health management process.

Key Technologies

Key technologies developed to support Gas path health management include the following.

Propulsion Diagnostic Method Evaluation Strategy (ProDiMES)

A standard benchmarking problem and evaluation metrics to enable the comparison of candidate aircraft engine gas path diagnostic methods

Many of the propulsion gas path diagnostic method solutions published in the open literature are applied to different platforms, with different levels of complexity, addressing different problems, and using different metrics for evaluating performance. As such, it is difficult to perform a one-to-one comparison of candidate approaches. Furthermore, these inconsistencies create barriers to effective development of new algorithms and the exchange of results.

To help address these issues, the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) software tool has been specifically designed with the intent to be made publicly available. In this form it can serve as a reference, or theme problem, to aid in propulsion gas path diagnostic technology development and evaluation.

The overall goal is to provide a tool that will serve as an industry standard and will truly facilitate the development and evaluation of significant Engine Health Management (EHM) capabilities. ProDiMES has been developed under a collaborative project of The Technical Cooperation Program (TTCP) based on feedback provided by individuals within the aircraft engine health management community.

ProDiMES Benchmarking Process ProDiMES Benchmarking Process

The ProDiMES tool is coded in MATLAB (The Mathworks, Inc.), and consists of the following functions:

ProDiMES benchmarking process flow chart
ProDiMES benchmarking process.

Requesting Access to ProDiMES

ProDiMES is available through the NASA Software Repository

Optimal Tuner Selection for Self-Tuning Engine Models


An emerging approach within the aircraft engine community is the inclusion of adaptive on-board engine models embedded within engine control computer. These models typically include a Kalman filter-based tracking filter that tunes the model to match the physical engine performance based on available sensor measurements. The benefits of self-tuning on-board engine models includes:


The aircraft engine performance estimation problem poses an underdetermined estimation problem where there are more unknowns than available sensor measurements.


To address the underdetermined estimation problem NASA has developed an “optimal tuner” selection methodology that has been shown to significantly improve on-board engine performance estimation accuracy in the presence of turbomachinery deterioration. This methodology constructs an optimal tuning parameter vector that is:

Graphs demonstrating thrust estimation accuracy comparison of conventional vs. optimal tuner selection approach.
Thrust estimation accuracy comparison of conventional vs. optimal tuner selection approach.

Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics


Conventional aircraft gas turbine engine gas path health management approaches:

Emerging Diagnostics Approach

NASA’s Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics

Provides an architecture that analyzes streaming measurement data and performs combined performance trend monitoring and gas path diagnostics.

Information Fusion


Information fusion approaches leverage information available from multiple sources to yield improved accuracy and confidence in engine health management inferences. NASA has worked to develop and apply information fusion approaches in past partnerships with Pratt & Whitney and Honeywell.


Information Sources

Information fusion architecture flow chart graphic
Information fusion architecture.

Impact of Environmental Particulate Ingestion on Aircraft Engine Performance


A number of in-flight aircraft engine power loss events have occurred due to the ingestion of ice crystals or volcanic ash.


The NASA Glenn Intelligent Control and Autonomy Branch has partnered in the modeling and analysis of engine system level performance effects caused by engine icing and volcanic ash ingestion.

Graph demonstrating normalized measurements acquired during icing-induced engine rollback event.
Normalized measurements acquired during icing-induced engine rollback event.

Space Propulsion Health Management

The primary objective of Space Propulsion Health Management (SPHM) is to provide vehicle propulsion systems with the capabilities to preserve the vehicle’s ability to achieve mission goals, which is to ensure safe operation by:

SPHM spans the spectrum of a vehicle’s lifecycle from development to operations, and may include:

GRC has more than 30 years of experience developing, implementing, and deploying system health management technologies for launch vehicle flight and ground systems. These technologies provide a broad scope of capabilities, which includes

Application of these capabilities helps to ensure that NASA systems operate safely, reliably, and with greater availability, in order to provide mission success.

Space propulsion health management historical timeline

Flight Computer Algorithm Development

GRC-developed Space Propulsion Health Management (SPHM) algorithms for flight computer applications provide the following mission and fault management (M&FM) capabilities for space vehicles:

Sensor Data Qualification and Consolidation


Avionics hardware redundancies provide fault tolerant flight-critical sensor measurements to assure sufficient functionality in the event of component failure. These measurements may be characterized as follows.

Sensor Data Qualification and Consolidation (SDQC) algorithms are implemented to:

Avionics hardware redundancies diagram


The design, implementation, and verification of SDQC algorithms has a number of significant challenges.


GRC has developed Sensor Data Qualification and Consolidation (SDQC) algorithms for the Space Launch System (SLS) flight computers. These are real-time algorithms that process flight-critical sensor data prior to use of the data for onboard vehicle control and decision-making. The algorithms do this by:

Flow chart showing the data qualification process.

Functional Fault Models


Functional Fault Models (FFM) are directed graph-based models designed to provide a qualitative representation of failure effect propagation paths within a given system architecture. Failures can be propagated from their source to the sensors available to detect the failure effects. Unique features of these models include the following.

The figure below provides an example of a directed graph model. Failure mode effects FM1, FM2, and FM3 in the electrical component block on the left are transformed as they propagate through various components to the sensor element block on the right. Along the propagation path, tests TP1, TP2, and TP3 are used to detect the failure mode effects. The completed graph can be followed in reverse to identify the failure mode or modes from a specific set of tests.

Functional fault model development flow chart



FFMs are developed using the following information:

FFM Subsystem Model flow chart

Recent NASA projects have developed qualitative FFMs using a commercial modeling tool that includes both a graphical user interface to create FFMs and built-in analysis processing to perform diagnostic assessments (i.e. detection coverage and failure isolation). For the NASA Space Launch System (SLS) Mission and Fault Management (M&FM) project, initial FFMs were developed at the subsystem-level and then integrated into the larger vehicle-level FFM. A recent version of this FFM, SLS Integrated Vehicle Failure Model (IVFM), contained over 40,000 failure modes.

Computer screen showing software program in use

Applications & Tools

FFMs have been used to support the analysis of failure management systems, verification of failure detection coverage, and online or off-line failure detection and classification.

More recent NASA FFM applications/demonstrations:

FFM Support Tools

The following tools are available to support the development and use of FFMs.

Goal Tree/Success Tree


Goal Tree/Success Tree (GT/ST) is a functional decomposition framework for modeling complex physical systems.



Goal tree/success tree diagram

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