A key function of rotorcraft Health and Usage Monitoring Systems (HUMS) is to monitor the condition or health of drive systems or mechanical drivetrains for transmitting power from a power source, for example a turbine-based engine, to one or more rotor systems used to provide aerodynamic lift, propulsion, and vehicle control. A rotorcraft drive train typically consists of gearboxes that change shaft rotational speed or direction, drive shafts that connect gearboxes to the power source or each other, and external bearings that support drive shafts that transmit power over long distances. Drive train gearboxes typically consist of internal gears, bearings, and shafts. Traditionally, the condition of each drivetrain component or subcomponent is monitored primarily through diagnostics-based analysis of changes in vibration signatures due to mechanical faults. The challenge of vibration-based drivetrain diagnostics is that vibration signatures are often very sensitive to other factors such as, for example, the power being transmitted that changes as a function of rotorcraft configuration (e.g., weight and center-of-gravity) and operating condition (e.g., flight speed, rate of climb, flight maneuver). Therefore, HUMS are typically designed to collect data in a way that allows direct comparison and trending of vibration features that are translated into condition indicators (CIs) for various component failure modes. The health of a drivetrain component is a function of the CIs for all the subcomponents and associated failure modes.
HUMS typically collect and process vibration information using one of two acquisition strategies. In the first approach, some HUMS acquire or capture vibration data continuously without regard to flight conditions. This has the advantage of acquiring many data points during a flight, but difficulty in trending vibration features or condition indicators (CIs) derived from these features because they are sensitive to variations in drivetrain loads that vary throughout the flight envelope. The high variability in vibration features or CIs and difficulty in trending them typically result in increased thresholds associated with increased damage states to achieve acceptable probability of detection and false alarm rates. In the second approach, some HUMS use regime-based data capture windows, typically acquiring vibration data only during steady-state operating conditions, such as ground runs, hover, and steady-level flight. The advantages of the second regime-based approach are reduced variability in vibration features or CIs within each regime, improved trending, and clearer detection of a change of component condition. However, the disadvantage is that the steady-state operating conditions typically occur at relatively moderate loads such that faults often do not manifest themselves until growing fairly large. Because the regime based capture windows are not a direct indication of load, there is still variability in the vibration features and CIs within a regime. For example, drivetrains must transmit higher power, using higher torque when flying at nominally the same speed at maximum gross weight versus minimum gross weight.
A third approach, which heretofore has been impractical, would be to acquire data during high-load maneuvers or regimes where many faults will manifest themselves as detectable changes in vibration features much sooner than they would during moderate load, steady-state regimes. The difficulty of this approach is that these higher loads often occur during transient maneuvers or operating states, which have even higher variability in terms of loads depending on aircraft configuration and pilot technique in flying the maneuver. Further, traditional steady-state signal processing methods are not appropriate for extracting vibration features from transient or dynamic structural vibratory responses and thus require advanced dynamic signal processing methods.
Other challenges faced by any vibration-based drive-system diagnostic approach is the additional variability and uncertainty manifested in vibration features and CIs as the result of inherently noisy vibration signals and occasional data quality issues that can result from degraded or faulted sensors, where said faults may be present only intermittently. Due to all the aforementioned (e.g., loads, noise, faulty sensors) as well as other sources of variability and uncertainty, HUMS vibration-based diagnostics typically rely on static thresholds that are relatively high to ensure confident detection of critical faults and acceptable false alarm rates. This has resulted in fairly short detection lead times that help avoid in-flight detection of critical faults and mission aborts, but have not resulted in as much improvement as desired in reduced cost of maintenance because of the lack of time to plan maintenance or order high-value spare parts.
Statistical change detection (SCD) algorithms have been developed to enable better and earlier detection of incipient faults, allowing longer detection lead times, but these SCD algorithms are still challenged by the aforementioned variability and uncertainties in CIs. Finally, advanced practical torque measurement technologies are becoming available, both physical sensor and virtual sensors that enable advanced methods of acquiring, processing, and trending vibration data.
There is a desire for a new holistic approach that exploits new enabling technologies, exploits the advantages of traditional vibration-based diagnostics, and addresses the inherent challenges of vibration-based drive system diagnostics, allowing earlier detection of drivetrain component faults, while maintaining acceptable false alarm rates, leading to longer detection lead times, reduced unscheduled maintenance, higher aircraft availability, optimized supply chain management, and reduced operational cost. This requires advanced methods for data capture, signal processing, and trending to increase the sensitivity and reduce the variability of both traditional steady-state and advanced high-load vibration features and CIs, along with advanced diagnostic algorithms for fusing traditional and new vibration features and CIs to achieve higher accuracy diagnostics, improved fault isolation, and desired improvements in maintenance efficiency.