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Adaptive state detection of gearboxes under varying load conditions based on parametric modelling

机译：基于参数建模的变速箱在不同负载条件下的自适应状态检测

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摘要

Conventional vibration monitoring techniques are unable to provide accurate state analysis of a gearbox under varying load condition. This paper proposes a novel technique for state detection of gearbox, which fits a time-varying autoregressive model to the gear motion residual signals applying a noise-adaptive Kalman filter, in the healthy state of the target gear. The optimum autoregressive model order, which provides a compromised model fitting for the healthy gear motion residual signals collected under various load conditions, is determined with the aid of a specific model order selection, method proposed in this study. Consequently, a robust statistical measure, which takes the percentage of outliers exceeding the three standard deviation limits is applied to evaluate the state of the target gear, where the standard deviation of autoregressive model residuals takes its maximum in all tested gear motion residual signals for model order selection. The proposed technique is validated using full lifetime vibration data of gearboxes operating from new to failure under four distinct load conditions. The investigated load conditions include: (1) constant load, (2) one jump from 100 to 200% nominal torque level, (3) one jump from 100 to 300% nominal torque level, and (4) constant changed to sinusoidal. In each application, the specific model order selection and comparison of the proposed gear state indicator with three counterparts proposed in recent studies are addressed in detail. The Kolmogorov-Smirnov test is also performed as a complementary statistical analysis. The results show that the proposed technique possesses a highly effective and robust property in the state detection of gearbox, which is independent of varying load condition as well as remarkable stability, early alarm for incipient fault and significant presence of fault effects. The proposed gear state indicator can be directly employed by an on-line maintenance program as a reliable quantitative covariate to schedule optimal maintenance decision for rotating machinery.
机译：传统的振动监测技术无法在变化的负载条件下提供变速箱的准确状态分析。本文提出了一种用于齿轮箱状态检测的新技术，该技术在目标齿轮的健康状态下，采用自适应噪声的卡尔曼滤波器，将时变自回归模型与齿轮运动残差信号进行拟合。最优自回归模型阶次是针对特定负载下收集的健康齿轮运动残差信号提供折衷的模型拟合，该阶跃模型是通过本研究提出的特定模型阶次选择来确定的。因此，采用了一种健壮的统计方法，该方法采用离群值超出三个标准偏差极限的百分比的方式来评估目标齿轮的状态，其中自回归模型残差的标准偏差在模型的所有测试齿轮运动残差信号中均达到最大值订单选择。变速箱在四个不同的负载条件下从新运转到失效的整个寿命振动数据，对所提出的技术进行了验证。研究的负载条件包括：（1）恒定负载；（2）从100至200％额定转矩水平一跳；（3）从100至300％额定转矩水平一跳；以及（4）常数变为正弦曲线。在每种应用中，都会详细讨论特定模型的顺序选择以及拟议的齿轮状态指示器与最近研究中提出的三个对应模型的比较。 Kolmogorov-Smirnov检验也作为补充统计分析进行。结果表明，所提出的技术在变速箱状态检测中具有高效，鲁棒的特性，并且与载荷条件的变化无关，并且具有显着的稳定性，早期故障的早期警报和明显的故障影响。提出的齿轮状态指示器可以由在线维护程序直接用作可靠的定量协变量，以安排旋转机械的最佳维护决策。

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