Improving Diagnosability of Hybrid Systems through Active Diagnosis

Fault diagnosis is key to ensuring system safety through fault-adaptive control. This task is diffcult in hybrid systems with combined continuous and discrete behaviors because mode changes make diagnosability hard to achieve. Including additional sensors can improve diagnosability, but that is not always feasible. An alternative strategy is active diagnosis, where we improve the diagnosis result by executing or blocking controllable events. We present a qualitative, event-based approach to active diagnosis of hybrid systems, where we automatically synthesize event-based diagnosers for hybrid systems that can determine if the system is diagnosable through passive or active diagnosis. We apply our active diagnosis scheme to a real-world electrical power distribution system.

Data and Resources

Additional Info

Field Value
Maintainer Matthew Daigle
Last Updated March 31, 2025, 14:21 (UTC)
Created March 31, 2025, 14:21 (UTC)
accessLevel public
accrualPeriodicity irregular
bureauCode {026:00}
catalog_@context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
catalog_@id https://data.nasa.gov/data.json
catalog_conformsTo https://project-open-data.cio.gov/v1.1/schema
catalog_describedBy https://project-open-data.cio.gov/v1.1/schema/catalog.json
harvest_object_id 67447bbb-fc79-4fbd-82c4-f6600927e54f
harvest_source_id 61638e72-b36c-4866-9d28-551a3062f158
harvest_source_title DNG Legacy Data
identifier DASHLINK_887
issued 2014-01-07
landingPage https://c3.nasa.gov/dashlink/resources/887/
modified 2020-01-29
programCode {026:029}
publisher Dashlink
resource-type Dataset
source_datajson_identifier true
source_hash a8eb6c8d700572331e723f8d430b999757df8d15de1fdc21a8798285ec996c0b
source_schema_version 1.1