基于机器学习的旋转轴振动不平衡检测
Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration Data
摘要：
在振动传感器的帮助下，旋转机械的故障检测提供了在早期阶段检测机器损坏并通过采取适当措施防止生产停机的可能性。使用机器学习方法对振动数据进行分析，可以显著减少相关的分析工作，并进一步提高诊断准确性。在这里，我们发布了一个数据集，它被用作不平衡检测算法的开发和评估的基础。为此，各种尺寸的不平衡使用3D打印的支架连接到旋转轴上。在速度范围从大约。630 RPM至2330 RPM，三个传感器用于以每秒4096个值的采样率记录旋转轴上的振动。开发和评估数据集可用于每个不平衡强度。使用以这种方式记录的数据集，测试了基于自动提取的时间序列特征的全连接和卷积神经网络、隐马尔可夫模型和随机森林分类。在评估数据集上的预测准确率为98.6%的情况下，可以使用接收缩放的FFT变换的振动数据作为输入的全连接神经网络来实现最佳结果。
英文摘要：
Fault detection at rotating machinery with the help of vibration sensors offers the possibility to detect damage to machines at an early stage and to prevent production downtimes by taking appropriate measures. The analysis of the vibration data using methods of machine learning promises a significant reduction in the associated analysis effort and a further improvement in diagnostic accuracy. Here we publish a dataset which is used as a basis for the development and evaluation of algorithms for unbalance detection. For this purpose, unbalances of various sizes were attached to a rotating shaft using a 3D-printed holder. In a speed range from approx. 630 RPM to 2330 RPM, three sensors were used to record vibrations on the rotating shaft at a sampling rate of 4096 values per second. A development and an evaluation dataset are available for each unbalance strength. Using the dataset recorded in this way, fully connected and convolutional neural networks, Hidden Markov Models and Random Forest classifications on the basis of automatically extracted time series features were tested. With a prediction accuracy of 98.6 % on the evaluation dataset, the best result could be achieved with a fully-connected neural network that receives the scaled FFT-transformed vibration data as input.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
