Detection of rolling-element bearing faults is crucial for implementing proactive maintenance strategies and for minimizing the economic and operational consequences of unexpected failures. However, many existing techniques are developed and tested under strictly controlled conditions, limiting their adaptability to the diverse and dynamic settings encountered in practical applications. This paper presents an efficient real-time convolutional neural network (CNN) for diagnosing multiple bearing faults under various noise levels and time-varying rotational speeds. Additionally, we propose a novel Fisher-based spectral separability analysis (SSA) method to elucidate the effectiveness of the designed CNN model. We conducted experiments on both healthy bearings and bearings afflicted with inner race, outer race, and roller ball faults. The experimental results show the superiority of our model over the current state-of-the-art approach in three folds: it achieves substantial accuracy gains of up to 15.8%, it is robust to noise with high performance across various signal-to-noise ratios, and it runs in real-time with processing durations five times less than acquisition. Additionally, by using the proposed SSA technique, we offer insights into the model's performance and underscore its effectiveness in tackling real-world challenges.

翻译：滚动轴承故障检测对于实施主动维护策略以及最小化意外故障带来的经济和运营后果至关重要。然而，许多现有技术是在严格控制的条件下开发和测试的，限制了其对实际应用中多样化动态环境的适应性。本文提出了一种高效的实时卷积神经网络（CNN），用于在不同噪声水平和时变转速下诊断多种轴承故障。此外，我们提出了一种新颖的基于费舍尔准则的频谱可分性分析方法（SSA），以阐明所设计CNN模型的有效性。我们在健康轴承以及存在内圈、外圈和滚动体故障的轴承上进行了实验。实验结果表明，我们的模型相较于当前最先进方法在三个方面具有优越性：其准确率提升高达15.8%，对不同信噪比的噪声具有鲁棒性，且能以低于采集时间五倍的处理速度实现实时运行。此外，通过所提出的SSA技术，我们揭示了模型性能的内在机制，并强调了其在解决实际挑战中的有效性。