Intelligent fault detection and diagnosis, as an important approach, play a crucial role in ensuring the stable, reliable and safe operation of rolling bearings, which is one of the most important components in the rotating machinery. In real industries, it is common to face that the issues of severe data imbalance and distribution difference since the number of fault data is small and the equipments frequently change the working conditions according to the production. To accurately and automatically identify the conditions of rolling bearings, a normalized convolutional neural network is proposed for the diagnosis of different fault severities and orientations considering data imbalance and variable working conditions. First, the batch normalization is adopted as a novel application to eliminate feature distribution difference, which is the prerequisite for ensuring generalization ability under different working conditions. Then, a special model structure is established and the overall performances of the proposed model are optimized by iterative update, which combines the exponential moving average technology. Finally, the proposed model is applied to the fault diagnosis under different data imbalance cases and working conditions. The effectiveness of the proposed method is verified based on two popular experiment dataset, and the diagnosis performance is widely evaluated in different scenarios. Comparisons with other commonly used methods and related works on the same dataset demonstrate the superiority of the proposed method. The results show that the proposed method has excellent diagnosis accuracy and admirable robustness, and also has sufficient stability on the data imbalance.

智能故障检测和诊断作为一种重要的方法，对于确保滚动轴承的稳定，可靠和安全运行起着至关重要的作用，滚动轴承是旋转机械中最重要的组件之一。在实际工业中，由于故障数据数量少，设备经常根据生产情况改变工作条件，因此经常会遇到严重的数据不平衡和分布差异的问题。为了准确，自动地识别滚动轴承的状况，提出了一种归一化卷积神经网络，用于在考虑数据不平衡和可变工作条件的情况下，对不同的故障严重程度和方向进行诊断。首先，采用批量归一化作为一种​​新颖的应用程序来消除特征分布差异，这是确保在不同工作条件下具有泛化能力的前提。然后，建立了特殊的模型结构，并通过迭代更新（结合了指数移动平均技术）优化了所提出模型的整体性能。最后，将该模型应用于不同数据不平衡情况和工作条件下的故障诊断。基于两个流行的实验数据集验证了该方法的有效性，并在不同情况下对诊断性能进行了广泛的评估。与其他常用方法和相关工作在同一数据集上的比较证明了该方法的优越性。结果表明，该方法具有良好的诊断准确性和良好的鲁棒性，