Failure of roller bearings can cause downtime or a complete shutdown of rotating machines. Therefore, a well-timed detection of bearing defects must be performed. Modern condition monitoring demands simple but effective bearing failure diagnosis by integrating dynamic models with intelligence techniques. This paper presents an integration of Dimensional Analysis (DA) and Artificial Neural Network (ANN) to diagnose the size of the bearing faults. The vibration responses of artificially damaged bearings using Electrode Discharge Machining are collected using Fast Fourier Techniques on a developed rotor-bearing test rig. Two-performance indicators, actual error, and performance of error are used to evaluate the accuracy of models. The simplicity of the DA model and the performance of the ANN model predicting with 5.49% actual error and 97.79 performance of error band enhanced the accuracy of diagnosis compared to the experimental results. Moreover, ANN has shown good performance over experimental results and DA.

滚子轴承的故障会导致停机或旋转机器完全停机。因此，必须及时检测轴承缺陷。现代状态监测需要通过将动态模型与智能技术相结合来进行简单而有效的轴承故障诊断。本文提出了维数分析 (DA) 和人工神经网络 (ANN) 的集成来诊断轴承故障的大小。使用电极放电加工的人为损坏轴承的振动响应是在开发的转子轴承试验台上使用快速傅立叶技术收集的。使用两个性能指标，实际误差和误差的性能来评估模型的准确性。DA 模型的简单性和 ANN 模型预测的性能，实际误差为 5.49% 和 97。与实验结果相比，误差带的 79 性能提高了诊断的准确性。此外，人工神经网络在实验结果和 DA 上表现出良好的性能。