Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns. Bearing faults are primary contributors to machine vibrations, and analyzing their signal morphology offers insights into their health status. Unfortunately, existing approaches are optimized for controlled environments, neglecting realistic conditions such as time-varying rotational speeds and the vibration's non-stationary nature. This paper presents a fusion of time-frequency analysis and deep learning techniques to diagnose bearing faults under time-varying speeds and varying noise levels. First, we formulate the bearing fault-induced vibrations and discuss the link between their non-stationarity and the bearing's inherent and operational parameters. We also elucidate quadratic time-frequency distributions and validate their effectiveness in resolving distinctive dynamic patterns associated with different bearing faults. Based on this, we design a time-frequency convolutional neural network (TF-CNN) to diagnose various faults in rolling-element bearings. Our experimental findings undeniably demonstrate the superior performance of TF-CNN in comparison to recently developed techniques. They also assert its versatility in capturing fault-relevant non-stationary features that couple with speed changes and show its exceptional resilience to noise, consistently surpassing competing methods across various signal-to-noise ratios and performance metrics. Altogether, the TF-CNN achieves substantial accuracy improvements up to 15%, in severe noise conditions.

翻译：轴承故障诊断对于降低维护成本与减少运行中断至关重要。轴承故障是机器振动的主要诱因，通过分析其信号形态可洞察轴承健康状态。然而，现有方法多针对受控环境优化，忽视了时变转速与振动非平稳性等实际工况。本文提出一种融合时频分析与深度学习技术的轴承故障诊断方法，适用于时变转速与不同噪声水平场景。首先，我们建立了轴承故障诱发振动的数学模型，探讨了其非平稳性与轴承固有及运行参数之间的关联。同时，阐释了二次时频分布特性，并验证了其解析不同轴承故障相关动态模式的效能。基于此，我们设计了时频卷积神经网络（TF-CNN），用于滚动轴承多类型故障诊断。实验结果表明，TF-CNN在性能上显著优于近期发展的诊断技术。该方法不仅能有效捕获与转速变化耦合的故障相关非平稳特征，更展现出卓越的噪声鲁棒性，在不同信噪比与性能指标下均持续超越对比方法。在强噪声条件下，TF-CNN的识别精度最高提升达15%。