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首页> 外文会议>Technology solutions for affordable sustainment >INVESTIGATION AND EVALUATION OF CONDITION INDICATORS, VARIABLE SELECTION, AND HEALTH INDICATION METHODS AND ALGORITHMS FOR ROTORCRAFT GEAR COMPONENTS
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INVESTIGATION AND EVALUATION OF CONDITION INDICATORS, VARIABLE SELECTION, AND HEALTH INDICATION METHODS AND ALGORITHMS FOR ROTORCRAFT GEAR COMPONENTS

机译：齿轮齿轮组件状态指标的调查与评估，变量选择以及健康指示方法和算法

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Providing better fleet-wide availability, reliability, and maintainability, are some of the potential benefits of a rotorcraft health and usage monitoring system. Despite several advances in research and development on drivetrain condition monitoring and also many deployed rotorcraft health and usage monitoring systems, there is a still a significant need to further validate and improve upon the diagnostic and prognostic algorithms for drivetrain components. This study focuses on performing an investigation on gear condition indicators, methods for ranking and selecting the condition indicators, and also health indication algorithms that fuse multiple gear condition indicators into a single health index. For investigating the gear health monitoring algorithms, a spiral-bevel gear fatigue test-rig is used to conduct four run-to-failure experiments. Vibration measurements along with a tachometer pulse are collected throughout the life testing, and visual inspection and oil-debris measurements are used to compare the vibration symptom response with the actual gear health condition. Time domain statistics, time synchronous average spectrum indicators, amplitude modulation, residual signal, auto-regressive residual signal, and second order cyclostationary indicators are evaluated in this study. Three different variable ranking methods based on the Fisher criterion, the area under the receiver operating characteristic curve and the monotonic criteria were weighted together to rank and select the best performing condition indicators. The top performing condition indicators were from the residual and amplitude modulation signal, and consisted of the residual signal RMS, residual signal peak to peak value, the sideband level indicator, and the amplitude modulation peak to peak value. A principal component based health indicator provided the most consistent health trend when compared with the distribution overlap and auto-associative neural network methods. The future work will consider further validation of the algorithms with additional run-to-failure tests, and a more in depth study on the influence of load and speed on the condition indicators.
机译：提供更好的机队范围可用性，可靠性和可维护性，是旋翼飞机健康和使用情况监视系统的一些潜在好处。尽管在动力传动系统状态监测的研究和开发方面取得了一些进展，并且还有许多已部署的旋翼飞机健康和使用情况监测系统，但仍然仍然需要进一步验证和改进动力传动系统组件的诊断和预后算法。这项研究的重点是进行齿轮状态指示器，状态指示器的排序和选择方法以及将多个齿轮状态指示器融合为一个健康指标的健康指示算法的研究。为了研究齿轮健康监测算法，使用了一个螺旋锥齿轮疲劳试验台进行了四次从试验到失效的实验。在整个寿命测试中，都会收集振动测量值和转速计脉冲，并使用目视检查和油污测量值将振动症状响应与实际齿轮健康状况进行比较。本研究评估了时域统计量，时间同步平均频谱指标，幅度调制，残差信号，自回归残差信号和二阶循环平稳指标。根据Fisher准则，接收器工作特性曲线下的面积和单调准则对三种不同的变量分级方法进行加权，以对最佳性能条件指标进行分级和选择。表现最好的状态指示器来自残余和幅度调制信号，由残余信号RMS，残余信号峰到峰值，边带电平指示器以及幅度调制峰到峰值组成。与分布重叠和自动关联神经网络方法相比，基于主成分的健康指标提供了最一致的健康趋势。未来的工作将考虑通过额外的运行至失败测试进一步验证算法，并对负载和速度对状态指示器的影响进行更深入的研究。

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