Feasibility Study of a Rotorcraft Health and Usage Monitoring System (HUMS): Results of Operator's Evaluation

The objective was to evaluate the feasibility of a state-of-the-art health and usage monitoring system (HUMS) to provide monitoring of critical mechanical systems on the helicopter, including motors, drive train, engines, and life-limited components. The implementation of HUMS and cost integration with current maintenance procedures was assessed from the operator's viewpoint in order to achieve expected benefits from these systems, such as enhanced safety, reduced maintenance cost, and increased availability. An operational HUMS that was installed and operated under an independent flight trial program was used as a basis for this study. The HUMS equipment and software were commercially available. Based on the results of the feasibility study, the HUMS used in the flight trial program generally demonstrated a high level of reliability in monitoring the rotor system, engines, drive train, and life-limited components. The system acted as a sentinel to warn of impending failures. A worn tail rotor pitch bearing was detected by HUMS, which had the capability for self testing to diagnose system and sensor faults. Examples of potential payback to the operator with HUMS were identified, including reduced insurance cost through enhanced safety, lower operating costs derived from maintenance credits, increased aircraft availability, and improved operating efficiency. The interfacing of HUMS with current operational procedures was assessed to require only minimal revisions to the operator's maintenance manuals. Finally the success in realizing the potential benefits from HUMS technology was found to depend on the operator, helicopter manufacturer, regulator (FAA), and HUMS supplier working together

Role of On-Board Sensors in Remaining Life Prognostic Algorithm Development for Selected Assemblies as Input to a Health and Usage Monitoring System for Military Ground Vehicles

Improved reliability of military ground vehicle systems is often in direct conflict with increased functionality and performance. Health and Usage Monitoring Systems or HUMS are being developed to address this issue. HUMS can be practically defined as a system of sensors, processors and algorithms that give an indication of remaining component life. Fatigue of metal components is a common failure mode on military vehicles, and failures of this type have a major effect on vehicle reliability and availability. The purpose of this research is to develop the methods and algorithms necessary for applying HUMS and remaining life prognostics to metal fatigue on a military wheeled vehicle. A range of models were developed and fidelity of the models was shown to be correlated with computational complexity. Simplistic models based on feature recognition had the least potential for accurate fatigue damage predictions while high fidelity physics-based models had the most potential. Recommendations for the information needed to select the most appropriate model for a component and optimize the effect on vehicle reliability and availability were discussed. Methods for identifying the set of instrumentation that could reasonably be used as part of a HUMS and techniques for selecting the instrumentation that provides inputs for metal fatigue damage models were evaluated. Techniques for identifying critical data and instrumentation were also described. The methods and algorithms developed were demonstrated for a variety of components on a military wheeled vehicle, and validation was performed by comparing the results of the remaining life prognostics with those from high fidelity physics of failure models. The processes developed could be easily adapted to other platforms including commercial fleets of vehicles or aircraft. These algorithms and techniques provide potential for improving reliability and availability, but it should be noted that other methods may be more appropriate depending on the specific vehicle and failure mode. Significant work remains to implement HUMS technologies on a military wheeled vehicle, but increasing reliability and availability is a worthy goal

Using integrated mechanical diagnostics health and usage management system (IMDHUMS) data to predict UH-60L electrical generator condition

Military aircraft maintenance methods are moving from practices based on hard-time inspection and replacement intervals to one of Condition Based Maintenance (CBM). Benefits of CBM are the minimization of maintenance efforts and component replacement along with an increase in readiness and safety. Goodrich has developed the Integrated Mechanical Diagnostics Health and Usage Management System (IMD-HUMS) for the practices of CBM in helicopters. Great benefits have been realized with the IMD-HUMS system in regards to several maintenance practices, readiness, and safety. However, the total potential of the system in regards to these benefits for the multiple components observed by the IMDHUMS is not yet achieved. The IMD-HUMS gathers a great deal of pertinent, important data on the condition of multiple components and systems, but the meaning and full potential of all this data is not yet fully realized. The purpose of this research is to conduct and document a statistical analysis of IMD-HUMS produced data. Statistical applications of data mining, regression and classification trees are explored. The approaches used in the exploration of the IMD-HUMS acquired data sets are based on six electrical generators which displayed degradation or failure and hence required maintenance actions compared with sixty others which did not.http://archive.org/details/usingintegratedm109452957Approved for public release; distribution is unlimited

A methodology using health and usage monitoring system data for payload life prediction

© 2018 The Author(s).This paper presents a methodology to monitor the fatigue life of aerospace structures and hence the remaining allowable fatigue life. In fatigue clearance, conservative load assumptions are made. However, in reality, a structure may see much lower loads and so would be usable for much longer. An example ofthis is air carried guided missiles. In the UK, missiles must be decommissioned after a period of carriage. The implementation of a system that can monitor the usage of a missile during its time in service is advantageous to the military customer and provides a competitive advantage for the missile manufacture inexport markets where reduced through-life costs, longer in-service lives and increased safety are desired. The proposed methodology provides a means to monitor the service life of a missile. This paper describes how machine learning algorithms can be used with accelerometers to determine loads on a missile structure which would then be used to predict how long the missile has left in service

Automatic Threshold Setting and Its Uncertainty Quantification in Wind Turbine Condition Monitoring System

Setting optimal alarm thresholds in vibration based condition monitoring system is inherently difficult. There are no established thresholds for many vibration based measurements. Most of the time, the thresholds are set based on statistics of the collected data available. Often times the underlying probability distribution that describes the data is not known. Choosing an incorrect distribution to describe the data and then setting up thresholds based on the chosen distribution could result in sub-optimal thresholds. Moreover, in wind turbine applications the collected data available may not represent the whole operating conditions of a turbine, which results in uncertainty in the parameters of the fitted probability distribution and the thresholds calculated. In this study, Johnson, Normal, and Weibull distributions are investigated; which distribution can best fit vibration data collected from a period of time. False alarm rate resulted from using threshold determined from each distribution is used as a measure to determine which distribution is the most appropriate. This study shows that using Johnson distribution can eliminate testing or fitting various distributions to the data, and have more direct approach to obtain optimal thresholds. To quantify uncertainty in the thresholds due to limited data, implementations with bootstrap method and Bayesian inference are investigated

Low airspeed systems for the naval SH-60 Seahawk aircraft

Pitot-static systems have long been used to measure helicopter airspeed. The Pitot-static system is inaccurate at low airspeeds (below 40 knots) due to the limited sensitivity of the sensor and interference of rotor down wash. Additionally, the Pitot-static system only measures unidirectional airspeed and unlike its fixed wing counterparts the helicopter is not limited to flight in one direction. With the changing roles of the US Navy Seahawk it is imperative that the pilot and aircrew have all the information necessary to safely complete the mission and prolong the life of the aircraft and dynamic components. With the addition of a dipping sonar to the remanufactured SH-60B aircraft (designated SH- 60R) and the conduct of combat search and rescue mission in the Navy\u27s Seahawks the aircraft will spend more time in a hover and will be flown more aggressively than in the past. This thesis examiness the advantages of incorporating a low airspeed system into the modem helicopter, in particular the SH-60 Seahawk. The author examines the low airspeed sensors and systems currently available and gives a brief description of each system\u27s operation. The author examines the challenges of installing a low airspeed sensor onto the SH-60 Seahawk. The author has determined that either a laser velocimeter or an analytical neural network system would be the best approach for a low airspeed system for the SH-60 Seahawk. The author recommends a combined approach be taken to develop both the laser velocimeter and analytical neural network, and incorporate the best system after further flight testing

IVHM Related Research at NASA Glenn

The presentation summarizes Glenn Research center work in IVHM related areas. The focus is on the model-based engine control and diagnostics work being done at the Controls and Dynamics Branch

An analysis of the integrated mechanical diagnostics health and usage management system on rotor track and balance

This thesis is concerned with the operational benefit of the Integrated Mechanical Diagnostics Health and Usage Management Systems (IMD HUMS) rotor track and balance (RTB) functionality. The questions addressed are whether there is a savings in flight hours expended on functional check flights (FCF's) when compared to present practices, if there will there be a reduction in directed maintenance man-hours (DMMH) spent on maintenance related to the rotor system, and the impact on Operational Availability. Experiments were conducted using a discrete event simulation model of squadron flight operations and organizational level maintenance. The simulation is generic and can be used in the analysis of other helicopters. Input parameters governing the distributions of maintenance action inter-arrival times were estimated from Naval Aviation Logistics Data Analysis (NALDA) databases and squadron experiences on such systems. The analysis suggests that flight hours spent in FCF are dependent upon vibration growth rate, an unknown quantity, and the maintenance policy for rotor smoothing. Directed maintenance man-hours decrease with increasing numbers of IMD HUMS configured aircraft and further gains are achieved with a maintenance policy suited to a continuous monitoring system.http://archive.org/details/annalysisofinteg109451524Captain, United States Marine CorpsApproved for public release; distribution is unlimited

Polyspectral Signal Analysis Techniques For Condition Based Maintenance of Helicopter Drive-Train System

For an efficient maintenance of a diverse fleet of air- and rotorcraft, effective condition based maintenance (CBM) must be established based on rotating components monitored vibration signals. In this dissertation, we present theory and applications of polyspectral signal processing techniques for condition monitoring of critical components in the AH-64D helicopter tail rotor drive train system. Currently available vibration-monitoring tools are mostly built around auto- and cross-power spectral analysis which have limited performance in detecting frequency correlations higher than second order. Studying higher order correlations and their Fourier transforms, higher order spectra, provides more information about the vibration signals which helps in building more accurate diagnostic models of the mechanical system. Based on higher order spectral analysis, different signal processing techniques are developed to assess health conditions of different critical rotating-components in the AH-64D helicopter drive-train. Based on cross-bispectrum, quadratic nonlinear transfer function is presented to model second order nonlinearity in a drive-shaft running between the two hanger bearings. Then, quadratic-nonlinearity coupling coefficient between frequency harmonics of the rotating shaft is used as condition metric to study different seeded shaft faults compared to baseline case, namely: shaft misalignment, shaft imbalance, and combination of shaft misalignment and imbalance. The proposed quadratic-nonlinearity metric shows better capabilities in distinguishing the four studied shaft settings than the conventional linear coupling based on cross-power spectrum. We also develop a new concept of Quadratic-Nonlinearity Power-Index spectrum, QNLPI(f), that can be used in signal detection and classification, based on bicoherence spectrum. The proposed QNLPI(f) is derived as a projection of the three-dimensional bicoherence spectrum into two-dimensional spectrum that quantitatively describes how much of the mean square power at certain frequency f is generated due to nonlinear quadratic interaction between different frequency components. The proposed index, QNLPI(f), can be used to simplify the study of bispectrum and bicoherence signal spectra. It also inherits useful characteristics from the bicoherence such as high immunity to additive Gaussian noise, high capability of nonlinear-systems identifications, and amplification invariance. The quadratic-nonlinear power spectral density PQNL(f) and percentage of quadratic nonlinear power PQNLP are also introduced based on the QNLPI(f). Concept of the proposed indices and their computational considerations are discussed first using computer generated data, and then applied to real-world vibration data to assess health conditions of different rotating components in the drive train including drive-shaft, gearbox, and hanger bearing faults. The QNLPI(f) spectrum enables us to gain more details about nonlinear harmonic generation patterns that can be used to distinguish between different cases of mechanical faults, which in turn helps to gaining more diagnostic/prognostic capabilities

Health and Usage Monitoring: Autonomous Vehicles

This thesis presents a work in progress related to the use of Health and Usage Monitoring Systems (HUMS) data to actuate an adaptive control system on an autonomous vehicle operating in an Intelligent Transportation Systems (ITS). The autonomous passenger vehicle has rapidly matured from a speculative concept to a reality that is quickly appearing within our sightlines. Autonomous (also called self-driving, driverless, or robotic) vehicles have long been predicted in science fiction and discussed in popular science media. Recently, major corporations have announced plans to begin selling such vehicles in the near future, and some jurisdictions have passed legislation to allow such vehicles to operate legally on public roads. Autonomous vehicles will be performing intelligent functions (navigation, maneuver, behavior, or task) by perceiving the environment and implementing a responsive action based on HUMS input. Once these vehicles begin to operate on public roads as a norm, safety and reliability becomes a major factor. The implementation or expanded use of HUMS can perceivably render these systems reliable and safe to operate in any environment or mode. This thesis also depicts a notational framework for HUMS in autonomous vehicles operating on ITS networks and future research needed to make this a reality. Keywords: Health and Usage Monitoring System (HUMS), Reliability, Adaptive Systems, Prognostics, Autonomous Vehicle, Intelligent Transportation SystemM.S., Mechanical Engineering and Mechanics -- Drexel University, 201