V-22 Data Visualization Toolset (VDVT) Implementation

The V-22 Tiltrotor Vibration, Structural Life, and Engine Diagnostics (VSLED) unit and the Aircraft Maintenance Event Ground Station (AMEGS) are tools the maintainer uses during everyday operation. These tools support the V-22 maintenance objectives through the detection of existing or developing faults of predefined mechanical assemblies and components. All V-22 aircraft are equipped with VSLED units. The corresponding ground station supports individual aircraft or multiple aircraft attached to a squadron. This paper describes the latest activities on system configuration and functionality associated with mechanical systems monitoring (rotors, engines, interconnect shafting system and nacelle blower). The system fault detection and notification to the maintainer is primarily automated. Data review and analysis tools are available to the Fleet Support Team (FST) engineers and senior quality assurance (QA) maintainers to allow review and confirmation of the diagnostic indications. The paper focuses on the ongoing activities to enhance the present mechanical system diagnostic capabilities through integration of the ground station, Integrated Electronic Technical Manual (IETM), and V-22 data visualization toolset (VDVT). VSLED data has been used throughout the aircraft development and NAVAIR has recently approved VDVT for integration with the Collaborative Automated Maintenance Environment for Osprey (CAMEO). VDVT includes the capability to review data acquired during the flight. Flight parameters, vibration data (for example, discrete frequency data collection and spectral data), all types of on-board events such as engine events (surges, other operational events), rotor track and balance data, and structures and related events (landing events, flight regime identification, etc.) are recorded. Additional enhancements and anticipated further improvements of the on-board system and the ground station are addressed. Plans and activities for diagnostic improvements under CAMEO are also presented and an example of the system's performance in detection of degraded mechanical components is discussed.