Many of the ideas and concepts regarding the targeting of an “agile” maintenance strategy for System or the System of Systems (SoS) begin with the formalizing of a unified approach to collectively capture the functionality of the participating designs into a robust integrated systems’ design development and process. But this design development process must be able to fully assess not solely the top-down functional allocation Systems’ Design and Sustainment Requirements (Operational Availability, etc.) but also be fully positioned to assess and validate the functional and failure cause (root cause) propagation from the bottom-up.
When designing for any diagnostic paradigm or any combinations of diagnostic paradigms, or most specifically when coordinating on-board operational run-time diagnostics (typically performed by Health Monitoring Systems and Health Management Systems by using on-board BIT sensing technologies) with off-board maintenance activities, the eXpress approach will facilitate the coordination, integration and cross-validation of the design assessment products from each design discipline.
When the Start Up Built-In-Test (SBIT)reports back to the operator, or at the system or fielded product level, it is conveying the operational status, or health status of those specific subsystems that are involved in the proper functioning of the preliminary checkout of that equipment using the SBIT. Ultimately, we’ll need to examine precisely which functions are being examined by the SBIT or any other BIT, while the system or vehicle is operating in that particular state of operation, and as constrained by the functional test coverage by the specific SBIT.
Traditionally, Built-in-Test (BIT) has been assigned to “test” the presence of the proper functioning at various “testing locations or points”. Such test points have often been selected by the designer or the manufacturer based upon their specific expertise or available resources. If a more careful effort was to be required, then the determining of the BIT would require additional tools, technologies, expertise and then additional resources – meaning more cost and time.
The eXpress diagnostic modeling environment is essential for determining the diagnostic designs’ ability to “Uniquely Isolate” any failures (or loss of function). This capability, designated as “FUI” in eXpress, enables the assessment to determine if the design is able to isolate between the sensor and any of the functions contained on the object that is being sensed.
Predictive Maintenance, or “PdM”, can be considered to be the most advanced form of Preventative Maintenance, in referring to the optimizing of the sustainment approach for a fielded asset. It’s primary approach includes the developing of the methods and means to detect and rectify failures of an equipment or system sufficiently in advance of the failure(s). Predictive Maintenance infers that specifically-defined impending failures will be identified, or “prognosed” in advance or their “failure” as characterized and defined from an operational perspective. This approach may typically consider the development or reliance upon a variety of either existing or newly developed, specialized sensors and include the associated maintenance method(s) capable of providing such knowledge of the progression towards a realized failure.
DSI has focused on design data interoperability for more than thirty years. We realized that, should programs or organizations make an investment into the creation of any data artifacts during the design development or the design sustainment lifecycle(s) they’re covered in either or both lifecycle(s)!
In recent years, military and aerospace programs have dedicated significant resources toward research in advanced predictive maintenance technologies. In particular, much of the research focused on prognostics—developing sensors and measurements that they hope will not only improve system readiness, but also reduce the costs of product sustainment. Designed to identify incipient failures at the lowest levels of the system architecture, prognostic sensors are typically the end result of extremely detailed, yet extremely localized, physics-of-failure (“PoF”) analyses.