Advancement of Aging Rotorcraft Predictive Maintenance through Rationalization of Condition Monitoring and Data Analysis Techniques

This paper outlines ongoing and future efforts put forward by the Condition-Based Maintenance (CBM) Research Center at the University of South Carolina to improve diagnostic and prognostic capabilities of Digital Source Collector (DSC) systems (Vibration Management Enhancement Program (VMEP), Modern Signal Processing Unit (MSPU), and Integrated Health Monitoring System Health Usage Monitoring System (IVHMS-HUMS)) by rationalization of condition monitoring and data analysis techniques for CBM of drive train components of AH-64 and UH-60 model aircraft. The proposed research is characterized by an advanced signal processing technique that can quantify degradation of the helicopter power train by use of the data collected from stateof-the-art HUMS. Furthermore, proposed techniques are being extended to other signals such as acoustic, electrical, and temperature, so data fusion techniques can be applied in order to improve diagnostic performance and statistically predict system’s failure for a proactive maintenance. Explained in additional detail is the continued effort to model the AH-64 test system to more fully and realistically represent the actual conditions and lifetime of aircraft using novel and advanced signal processing techniques such as time-frequency domain information measure to assess the test-bed’s mechanical and electrical systems’ health that could lead to localized faults encountered by actual military and civilian aircraft. Further methods of increasing the likelihood of fault detection are discussed, including data fusion from varied sensing methods, providing enhanced detection of exceeded performance thresholds while eliminating undesirable disturbances caused by false alarms in health monitoring systems.