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和运行故障至关重要。轴承故障是机器振动的主要诱因，分析其信号形态可揭示健康状态。遗憾的是，现有方法针对受控环境优化，忽视了时变转速和振动非平稳性等现实条件。本文提出融合时频分析与深度学习技术，在时变转速和可变噪声水平下诊断轴承故障。首先，我们建立轴承故障诱发的振动模型，探讨其非平稳性与轴承固有参数及运行参数之间的关联。进一步阐明二次时频分布的特性，并验证其在解析不同轴承故障特有的动态模式中的有效性。基于此，我们设计了一个时频卷积神经网络（TF-CNN）用于诊断滚动轴承的各类故障。实验结果表明，TF-CNN相较于近期技术具有显著优越的性能。同时证实了其在捕捉与转速变化耦合的故障相关非平稳特征方面的通用性，并展现出卓越的抗噪鲁棒性，在各信噪比和性能指标下均持续优于对比方法。总体而言，TF-CNN在强噪声条件下实现了高达15%的精度提升。