In real-world applications, fault detection and diagnosis of planetary gearboxes are vital if it can be employed to avert catastrophic failure consequences in rotating machinery. Fault diagnosis usually starts with collecting vibration signals from rotating machinery. These vibration signals are usually produced in non-stationary operating conditions with time-varying loads and speeds, which makes fault diagnosis more challenging. Signal processing methods are typically selected for fault diagnosis to capture either time, frequency, or time-frequency based diagnostic features from measured vibration signals. Yet, it is usually a costly or time-consuming process and, sometimes, heavily dependent on human expertise. Although current deep learning algorithms offer an efficient and intelligent diagnostic strategy for fault diagnosis, unfortunately, most of the reported algorithms are basically only valid for the stationary operating conditions. To address the challenges of non-stationary operating conditions, in this paper, an Automatic Speed Adaption Neural Network (ASANN) model within the incorporation of instantaneous rotating speed is proposed, and it provides an end-to-end learning fashion with the guidance of rotating speed information. With the incorporating of instantaneous rotating speed information, the proposed ASANN model enables the extraordinary capacity for planetary gearbox fault detection under varying operational scenarios. The validity of the ASANN model is verified by an experimental investigation of fault diagnosis in a planetary gearbox.

在实际应用中，行星齿轮箱的故障检测和诊断对于避免旋转机械的灾难性故障后果至关重要。故障诊断通常从收集旋转机械的振动信号开始。这些振动信号通常是在不稳定的工况下产生的，其负载和速度会随时间变化，这使得故障诊断更具挑战性。通常选择信号处理方法进行故障诊断，以从测量的振动信号中捕获基于时间，频率或基于时频的诊断特征。然而，这通常是一个昂贵或耗时的过程，有时严重依赖于人类的专业知识。尽管目前的深度学习算法为故障诊断提供了有效且智能的诊断策略，但不幸的是，大多数报告的算法基本上只对固定工况有效。为了解决非平稳运行条件的挑战，本文提出了一种在瞬时转速内并入的自动速度自适应神经网络（ASANN）模型，该模型在以下指导下提供了端到端的学习方式：转速信息。通过结合瞬时转速信息，所提出的ASANN模型可以在各种运行情况下为行星齿轮箱故障检测提供非凡的能力。通过对行星齿轮箱中故障诊断的实验研究，验证了ASANN模型的有效性。为了解决非平稳运行条件的挑战，本文提出了一种在瞬时转速内并入的自动速度自适应神经网络（ASANN）模型，该模型在以下指导下提供了端到端的学习方式：转速信息。通过结合瞬时转速信息，所提出的ASANN模型可以在各种运行情况下为行星齿轮箱故障检测提供非凡的能力。通过对行星齿轮箱中故障诊断的实验研究，验证了ASANN模型的有效性。为了解决非平稳运行条件的挑战，本文提出了一种在瞬时转速内并入的自动速度自适应神经网络（ASANN）模型，该模型在以下指导下提供了端到端的学习方式：转速信息。通过结合瞬时转速信息，所提出的ASANN模型可以在各种运行情况下为行星齿轮箱故障检测提供非凡的能力。通过对行星齿轮箱中故障诊断的实验研究，验证了ASANN模型的有效性。它在转速信息的指导下提供了一种端到端的学习方式。通过结合瞬时转速信息，所提出的ASANN模型可以在各种运行情况下为行星齿轮箱故障检测提供非凡的能力。通过对行星齿轮箱中故障诊断的实验研究，验证了ASANN模型的有效性。它在转速信息的指导下提供了一种端到端的学习方式。通过结合瞬时转速信息，所提出的ASANN模型可以在各种运行情况下为行星齿轮箱故障检测提供非凡的能力。通过对行星齿轮箱中故障诊断的实验研究，验证了ASANN模型的有效性。