Recent development in the predictive maintenance field has focused on incorporating artificial intelligence techniques in the monitoring and prognostics of machine health. The current predictive maintenance applications in manufacturing are now more dependent on data-driven Machine Learning algorithms requiring an intelligent and effective analysis of a large amount of historical and real-time data coming from multiple streams (sensors and computer systems) across multiple machines. Therefore, this article addresses issues of data pre-processing that have a significant impact on generalization performance of a Machine Learning algorithm. We present an intelligent approach using unsupervised Machine Learning techniques for data pre-processing and analysis in predictive maintenance to achieve qualified and structured data. We also demonstrate the applicability of the formulated approach by using an industrial case study in manufacturing. Data sets from the manufacturing industry are analyzed to identify data quality problems and detect interesting subsets for hidden information. With the approach formulated, it is possible to get the useful and diagnostic information in a systematic way about component/machine behavior as the basis for decision support and prognostic model development in predictive maintenance.

预测性维护领域的最新发展集中在将人工智能技术纳入机器健康的监视和预测中。现在，制造业中当前的预测性维护应用程序更加依赖于数据驱动的机器学习算法，这些算法要求对来自多台机器中多个流（传感器和计算机系统）的大量历史和实时数据进行智能，有效的分析。因此，本文解决了对机器学习算法的泛化性能产生重大影响的数据预处理问题。我们提出了一种使用无监督机器学习技术的智能方法，以进行预测性维护中的数据预处理和分析，以获取合格的结构化数据。我们还通过在制造业中使用工业案例研究证明了所制定方法的适用性。分析来自制造业的数据集以识别数据质量问题并检测隐藏信息的有趣子集。通过制定方法，可以以系统的方式获得有关组件/机器行为的有用和诊断信息，以此作为预测性维护中决策支持和预测模型开发的基础。