Condition monitoring of gearboxes is integral to maintaining floor safety, system stability, and inventory management. Capturing vibration response using sensors and subsequent response analysis is the standard procedure for gearbox fault detection. However, the sensors are susceptible to non-constant reliability due to the convolution of vibration responses from multiple sources, background noise interference, and transfer-path effect. The problem is multi-fold when ideal sensor attachment locations are unavailable due to spatial constraints of industrial floors. The response component reflective of the fault information must be enhanced for adequate fault severity estimations. The present study addresses this hurdle by proposing a multi-sensor framework with available sensor attachment locations for gearbox condition monitoring. Adaptive filtering is done in the framework with parameters optimised to enhance fault information. A proximal policy optimisation agent is trained with a reinforcement learning environment for parameter refinement. Further, fault severity estimation is achieved by a weighted fusion of spectral features reflective of the side-band excitation effect caused by gear fault. The proposed method is applied to datasets acquired from an in-house seeded fault test bed. The proposed method underscores superior performance compared to conventional single-sensor-based fault severity analysis and alternate fusion approaches.

中文翻译：

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一种在变速箱状态监测中采用强化学习的自适应滤波方法，用于多传感器融合


齿轮箱的状态监测是维护地板安全、系统稳定性和库存管理不可或缺的一部分。使用传感器捕获振动响应并随后进行响应分析是变速箱故障检测的标准程序。然而，由于来自多个来源的振动响应的卷积、背景噪声干扰和传递路径效应，传感器容易受到非恒定可靠性的影响。当由于工业楼层的空间限制而无法获得理想的传感器连接位置时，问题就成倍了。必须增强反映故障信息的响应组件，以便进行适当的故障严重性估计。本研究通过提出一个多传感器框架来解决这一障碍，该框架具有用于变速箱状态监测的可用传感器连接位置。自适应过滤在框架中完成，并优化了参数以增强故障信息。近端策略优化代理使用强化学习环境进行训练，以进行参数优化。此外，通过对反映齿轮故障引起的边带激励效应的频谱特征进行加权融合，实现了故障严重性估计。所提出的方法适用于从内部种子故障测试台获取的数据集。与传统的基于单传感器的故障严重性分析和替代融合方法相比，所提出的方法强调了卓越的性能。