Purpose

The development and application of intelligent models to perform vibration-based condition monitoring in industry seems to be receiving attention in recent years. A number of such research studies using the artificial intelligence, machine learning, pattern recognition, etc., are available in the literature on this topic. These studies essentially used the machine vibration responses with known machine faults to develop smart fault diagnosis models. These models are yet to be tested for all kinds of machine faults and/or different operating conditions. Therefore, the purpose is to develop a generic machine faults diagnosis model that can be applied blindly to any identical machines with high confidence level in accuracy of the predictions.

Methods

In this paper, a supervised smart fault diagnosis model is developed. This model is developed using the available measured vibration responses for the different rotor faults simulated on an experimental rotating rig operating at a constant speed. The developed smart vibration-based machine learning (SVML) model is then blindly tested to identify the healthy and faulty conditions of the rig when operating at different speeds.

Results and conclusions

Several scenarios are proposed and examined during the development of the SVML model. It is observed that scenario of the vibration measurements simultaneously from all bearings from a machine is capable to fully map the machine dynamics in the VML model. Therefore, this developed when applied blindly to the sets of data at a different machine speed, the results are observed to be encouraging. The results clearly show a possibility for a centralised vibration-based condition monitoring (CVCM) model for identical machines operating at different rotating speeds.

中文翻译：

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开发的基于智能振动的机器学习（SVML）模型在不同机器条件下的机器故障诊断中的盲应用

目的

近年来，用于执行基于振动的状态监测的智能模型的开发和应用似乎正受到关注。有关人工智能，机器学习，模式识别等的许多此类研究都可以在该主题的文献中找到。这些研究本质上是利用已知机器故障的机器振动响应来开发智能故障诊断模型。这些模型尚未针对各种机器故障和/或不同的运行条件进行测试。因此，目的是要开发一种通用的机器故障诊断模型，该模型可以盲目地应用于预测准确度很高的任何相同机器上。

方法

本文提出了一种有监督的智能故障诊断模型。该模型是使用在恒定转速下运行的实验性旋转钻机上模拟的不同转子故障的可用测得的振动响应来开发的。然后，对开发的基于智能振动的机器学习（SVML）模型进行盲目测试，以识别以不同速度运行时钻机的健康状况和故障状况。

结果与结论

在SVML模型的开发过程中，提出并检查了几种方案。可以观察到，同时从一台机器的所有轴承进行振动测量的场景能够在VML模型中完全映射出机器动力学。因此，在以不同的机器速度盲目地应用到数据集时，这种方法得到了发展，结果令人鼓舞。结果清楚地表明，对于在不同转速下运行的相同机器，基于集中振动的状态监测（CVCM）模型是可行的。