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Smart Machine Maintenance Enabled by a Condition Monitoring Living Lab ?

机译：由状态监视生活实验室启用的智能机维护 ？

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A key barrier in the industrial adoption of condition monitoring is the lack of large and reliable data sets about the full lifetime of bearings in machines. This data is useful for model training as well as for validation purposes. This paper demonstrates how a living lab, consisting of 7 identical drive train sub-systems, can enable smart machine maintenance and support the adoption of condition monitoring technologies in the industry. The living lab allows to perform accelerated lifetime tests of bearings and to speed up the process of collecting large amounts of data about degrading bearings. It is shown that the data can be used to benchmark diagnostic algorithms. Three methods are compared: a data driven approach developed by the Linz Center of Mechatronics (LCM), a diagnostic method of Flanders Make (FM) and an approach developed by the Center for Intelligent Maintenance Systems (IMS). It is concluded that the method of IMS and FM, employing bearing specific features, tend to be slightly more sensitive to early detect bearing faults than the data driven approach employed by LCM. On the contrary, the method of LCM does not require specific system knowledge and is not limited to bearing monitoring only. The method is more widely applicable to fault monitoring problems. Besides a benchmark study, the living lab can also be used to develop, test and validate new diagnostic and prognostic methods. In this way, the living lab provides opportunities to enable a wider adoption of condition monitoring technologies in industry.
机译：工业上使用状态监测的一个主要障碍是缺乏有关机器轴承整个使用寿命的大型可靠数据集。此数据对于模型训练以及验证有用。本文演示了一个由7个相同的传动系子系统组成的居住实验室如何实现智能机器维护并支持行业中状态监测技术的采用。现场实验室允许执行轴承的加速寿命测试，并加快收集有关退化轴承的大量数据的过程。结果表明，该数据可用于基准诊断算法。比较了三种方法：林茨机电一体化中心（LCM）开发的数据驱动方法，佛兰德斯制造（FM）的诊断方法和智能维护系统中心（IMS）开发的方法。可以得出结论，与LCM所采用的数据驱动方法相比，采用轴承特定功能的IMS和FM方法对早期检测轴承故障更敏感。相反，LCM的方法不需要特定的系统知识，并且不仅限于轴承监视。该方法更广泛地应用于故障监测问题。除了基准研究之外，生活实验室还可以用于开发，测试和验证新的诊断和预后方法。这样，活动实验室为在工业中广泛采用状态监测技术提供了机会。

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