Downloaded from https://www.everyspec.com ADS-79D-HDBK item‘s condition and need for maintenance action. When this capability is extended to CBM functionality (state detection and prognosis assessment), it should have the following general characteristics: a. Sensor Technology: Sensors should have high reliability and high accuracy. There is no intent for recurring calibration of these sensors. b. Data Acquisition: Onboard data acquisition hardware should have high reliability. c. Sensor Selection: Sensors should be selected and/or designed in such a way that the predominate failure mode does not affect operational performance. d. Algorithms:
Fault detection algorithms are applied to the basic acquired data to provide condition and health indicators. Validation and verification of the Condition Indicators (CIs) and Health Indicators (HIs) included in the CBM system are required to establish maintenance and airworthiness credits/debits. Basic properties of the algorithms are: (1) sensitivity to the faulted condition, and (2) insensitivity to conditions other than the faulted condition. The algorithms and methodology should demonstrate the ability to account for exceedances, missing, or invalid data. Once verified and validated, there should be the presence of continuous assessment of algorithm performance.
Algorithms utilized as maintenance practice enhancements (versus a maintenance practice replacement) with reliability not verified and validated need only demonstrate a level of reliability acceptable to the platform manager and maintainer (i.e. acceptable level of maintenance associated with false positives). 4.2.2 Fatigue damage monitoring. Fatigue damage is estimated through calculations which use loads on aircraft components experienced during flight. These loads are dependent on environmental conditions (example, temperature and altitude), aircraft configuration parameters (examples:
gross weight (GW), center of gravity (CG)), external stores, and aircraft state parameters related to maneuvering (i.e.: air speed, aircraft attitudes, power applied, and accelerations). To establish these loads, regime recognition algorithms are used to take these parameters and map them to known aircraft maneuvers for which representative flight loads are available from loads surveys. In order to establish regime recognition algorithms as the basis for loads and retirement time adjustment, the algorithms should be validated through flight testing. Detail guidance for validation of regime recognition algorithms is contained in Appendix B.
Legacy aircraft operating without CBM capabilities typically use assumed usage, test established fatigue strength, and Safe Life calculation techniques to ensure airworthiness. Structural loading of the aircraft in flight, including instances which are beyond prescribed limits (i.e.: exceedances) for the aircraft or its components on legacy platforms typically use a rudimentary sensor or data from a cockpit display with required post-flight inspection as the means to assess damage.
The advent of data collection from aircraft sensors, typically performed onboard an aircraft by a Digital Source Collector (DSC) enables methods that improve accuracy of the previous detection and assessment methods. The improvement is due to the use of actual service usage or measured loads rather than calculations based on assumptions made during the developmental design phase of the acquisition. 4.2.3 Regime recognition (usage detection) Accurate detection and measurement of flight regimes experienced by the aircraft enable two levels of refinement for fatigue damage management:
(1) the baseline ―worst case‖ usage spectrum can be refined over time as the actual mission profiles and mission usage can be compared to the original design assumptions, and (2) individual component fatigue damage assessment estimates can be based on specific aircraft flight history instead of the baseline ―worst case design estimate‖ for the total aircraft population. To perform individual component fatigue 17