NASA has developed a method that prevents total system failure during emergencies, allowing parts of the system to continue to function, and making overall system recovery faster. A heterogeneous set of system components monitored by a varied suite of sensors and a health monitoring framework has been developed with the power and flexibility to adapt to different diagnostic and prognostic needs. Current state-of-the-art monitoring and health management systems are mostly centralized in nature, where all the processing is reliant on a single processor. This requires information to be sent and processed in one location. With increases in the volume of sensor data as well as the need for associated processing, traditional centralized systems tend to be somewhat ungainly; in particular, when faced with multi-tasking of computationally heavy algorithms. The distributed architecture is more efficient, allows for considerable flexibility in number and location of sensors placed, scales up well, and is more robust to sensor or processor failure.
The distributed health management architecture is comprised of a network of smart sensor devices. These devices monitor the health of various subsystems or modules. They perform diagnostics operations and trigger prognostics operation based on user-defined thresholds and rules. Both the diagnostic and prognostic tasks are formulated as a particle-filtering problem for state estimation and remaining life estimation, which also allows the explicit representation and management of uncertainties (but other suitable algorithms can also be used).
The sensor devices, called computing elements (CEs), consist of a sensor, or set of sensors, and a communication device (i.e., a wireless transceiver) beside an embedded processing element. The CEs can run both a diagnostic and prognostic operating mode. The diagnostic mode is the default mode where a CE monitors a given subsystem or component through a low-weight diagnostic algorithm. If a CE detects a critical condition during monitoring, it raises a flag. Depending on availability of resources, a networked local cluster of CEs is then formed that carries out prognostics and fault mitigation by efficient distribution of the tasks. The CEs are expected not to suspend their previous tasks in the prognostic mode. When the prognostics task is completed, and after appropriate actions have been taken, all CEs return to their original default configuration.
This technology has potential applications in prognostic health management, commercial aerospace and aircraft, mechanical systems, and process industries.