This paper provides an overview of current vibration methods used to identify the health of helicopter transmission gears. The gears are critical to the transmission system that provides propulsion, lift and maneuvering of the helicopter. This paper reviews techniques used to process vibration data to calculate conditions indicators (CI's), guidelines used by the government aviation authorities in developing and certifying the Health and Usage Monitoring System (HUMS), condition and health indicators used in commercial HUMS, and different methods used to set thresholds to detect damage. Initial assessment of a method to set thresholds for vibration based condition indicators applied to flight and test rig data by evaluating differences in distributions between comparable transmissions are also discussed. Gear condition indicator FM4 values are compared on an OH58 helicopter during 14 maneuvers and an OH58 transmission test stand during crack propagation tests. Preliminary results show the distributions between healthy helicopter and rig data are comparable and distributions between healthy and damaged gears show significant differences.
To enable Industry 4.0 successfully, there is a need to build a resilient automation system that can quickly recover after having been attacked or robustly sustain continued operations while being threatened, enable an automated monitoring evolution via various sensor channels in real time, and use advanced machine learning and data analytics to formulate strategies to mitigate and eliminate faults, threats, and malicious attacks. It is envisioned that if we can develop an intelligent model that (a) represents a meaningful, realistic environment and complex entity containing manufacturing Internet of Things interdependent and independent properties that are stepping-stones of the cyber kill chain or precursors of the onset of cyberattacks; (b) can learn and predict potential errors and formulate offense/defense strategies and healing solutions; (c) can enable cognitive ability and human-in-the-loop analytics in real time; and (d) can facilitate system behavior changes to disrupt the attack cascade, then the hosting system can learn how to neutralize threats and attacks and self-repair infected or damaged links autonomously. In this article, our preliminary work presents a visual analytics framework and technique for situational awareness, including autonomously monitoring, diagnosing, and prognosticating the state of cyber-physical systems. Our approach, presented in this article, relies on visual characterizations of multivariate time series and real-time predictive analytics to highlight potential faults, threats, and malicious attacks. To validate the usefulness of our approach, we demonstrate the developed technique using various aviation datasets obtained from the Prognostics Center of Excellence at the National Aeronautics and Space Administration Ames.
A B S T R A C T Fatigue crack growth predictions have been made on a helicopter round-robin crack configuration. The crack configuration was a small corner defect at the edge of a large central hole in a flanged plate made of 7010 aluminium alloy and the component was subjected to a simulated helicopter spectrum loading. The crack growth rate data and the stress-intensity factor (K) solution for the crack configuration were provided in the round-robin. The FASTRAN life-prediction code was used to predict fatigue crack growth under various load histories on the aluminium alloy, such as Rotorix and Asterix, on both compact tension C(T) specimens and the complex crack configuration. A BEASY threedimensional stress-intensity factor solution for the round-robin problem was also provided for this paper and is compared with the original K solution. Comparisons are made between measured and predicted fatigue crack growth lives for both crack configurations. The predicted lives for the C(T) specimens were 15-30% longer than the measured lives; and crack growth in the round-robin configuration agreed very well in the early stages of crack growth, but the life was 30% short of the test results at the final crack length. B = thickness, mm C i = coefficient in crack growth equation C 5 = cyclic fracture toughness, MPa-m 1/2 c = crack length, mm F = boundary correction factor on stress intensity factor K max = maximum stress intensity factor, MPa-m 1/2 N = cycles N e = experimental cycles N p = predicted cycles n i = power in crack growth equation q = constant in crack growth equation R = stress ratio (S min /S max ) r = hole radius, mm S max = maximum applied stress, MPa S min = minimum applied stress, MPa S o = crack opening stress level, MPa W = width, mm α = constraint factor K = stress intensity factor range, MPa-m 1/2 K eff = effective stress intensity factor range, MPa-m 1/2 σ ys = yield stress, MPa σ u = ultimate tensile strength, MPa
This paper discusses research conducted by the Army Research Laboratory (ARL) - Vehicle Technology Directorate (VTD) on advanced suspension control. ARL-VTD has conducted research on advanced suspension systems that will reduce the chassis vibration of ground vehicles while maintaining tire contact with the road surface. The purpose of this research is to reduce vibration-induced fatigue to the Warfighter as well as to improve the target aiming precision in-theater. The objective of this paper was to explore the performance effectiveness of various formulations of the generalized predictive control algorithm in a simulation environment. Each version of the control algorithm was applied to an identical model subjected to the same ground disturbance input and compared to a baseline passive suspension system. The control algorithms considered include a generalized predictive controller (GPC) with implicit disturbances, GPC with explicit disturbances, and GPC with preview control. The suspension model used was a two-degree-of-freedom dof quarter-car model with a given set of vehicle parameters. The performance of the control algorithms were compared based on their effectiveness in controlling peak acceleration and overall average acceleration over a range of vehicle speeds. The algorithms demonstrated significant reductions in the chassis acceleration of the quarter-car model.
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