Abstract Gearbox is one of critical transmission components in wind turbine (WT) having a high downtime rate among all subcomponents. Fault prognostics and health management (PHM) of WT gearbox is crucial to their high reliability operation. However, presence of background noise in WT signals restricts the applicability of existing PHM approaches in feature extraction. To solve this problem, a novel performance degradation assessment method based on deep belief network (DBN) and self-organizing map (SOM) is proposed to de-noise and merge multi-sensor vibration signals. Minimum quantization error (MQE) is defined as health indicator to detect incipient fault of WT gearbox. After health indicator construction, an improved particle filtering (PF) optimized by fruit fly optimization algorithm (FOA) is employed to predict the remaining use life (RUL) of WT gearbox. To take advantage of dynamic and random operation process of WT gearbox, Wiener-process-based degradation model is developed to improving RUL prediction efficiency. The effectiveness is validated by using simulated as well as experimental vibration signals obtained through a WT gearbox highly accelerated life test. The results illustrate that proposed method can evaluate performance degradation process and predict RUL of WT gearbox effectively.

中文翻译：

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基于混合 DBN-SOM-PF 的风力涡轮机齿轮箱剩余使用寿命预测方法

摘要 齿轮箱是风​​力发电机组（WT）中的关键传动部件之一，在所有子部件中具有很高的停机率。WT 齿轮箱的故障预测和健康管理 (PHM) 对其高可靠性运行至关重要。然而，WT 信号中背景噪声的存在限制了现有 PHM 方法在特征提取中的适用性。为了解决这个问题，提出了一种基于深度置信网络（DBN）和自组织图（SOM）的新的性能退化评估方法来对多传感器振动信号进行去噪和合并。最小量化误差 (MQE) 被定义为健康指标，用于检测 WT 齿轮箱的早期故障。健康指标构建后，通过果蝇优化算法 (FOA) 优化的改进粒子过滤 (PF) 用于预测 WT 齿轮箱的剩余使用寿命 (RUL)。为了利用WT齿轮箱的动态和随机运行过程，开发了基于Wiener过程的退化模型以提高RUL预测效率。通过使用通过 WT 齿轮箱高度加速寿命测试获得的模拟和实验振动信号来验证有效性。结果表明，所提出的方法可以有效地评估WT齿轮箱的性能退化过程并预测其RUL。通过使用通过 WT 齿轮箱高度加速寿命测试获得的模拟和实验振动信号来验证有效性。结果表明，所提出的方法可以有效地评估WT齿轮箱的性能退化过程并预测其RUL。通过使用通过 WT 齿轮箱高度加速寿命测试获得的模拟和实验振动信号来验证有效性。结果表明，该方法可以有效地评估WT变速箱的性能退化过程并预测其RUL。