Through the Internet of Things (IoT), the generation of data, Cyber-Physical Systems (CPS) has shown a steady increase. The search for approaches in order to take advantage of generated data is a recurring theme on several managers’ agenda. To this end, data mining techniques, combined with asset health management, contribute to Industry 4.0 relevance in production systems. In this context, the continuous process industry has a high maturity due to high-level instrumentation and control. This type of manufacturing has usually divided sensors into process control and equipment monitoring. Traditional reliability studies on development of models use sensor data to monitor the health of the equipment without counting on valuable process information. In contrast, this study proposes an approach that seeks to increase the reliability of a productive system, using data from operational control and health monitoring of equipment, which provides a more robust model of reliability. To this end, we used a semi-supervised Machine Learning (ML) with convolutional neural networks (CNN), Autoencoder (AE), and a bagged decision tree to identify which variables are responsible for an abnormal condition. With this approach, it is possible to detect which variables are most important in the occurrence of failures, enabling preventive actions that increase the reliability of the system. The main contribution of this approach is the integration of analytical techniques, in order to improve the reliability of the system. To test and validate the method, we performed a case study with real data in a styrene petrochemical plant. As a result, it was possible to identify the beginning of an anomaly that failed the vacuum system. With the proposed approach, mitigation actions could be taken and, consequently, avoid unnecessary downtime.

通过物联网（IOT），数据的产生，网络，物理系统（CPS）呈现稳步增长。为了利用所产生的数据的优势办法的搜索是在几个经理的议程中反复出现的主题。为此，数据挖掘技术，资产健康管理相结合，促进工业4.0相关的生产系统。在此背景下，持续的过程行业具有较高的成熟度，由于高水平的仪器仪表和控制。这种类型的制造通常将传感器分为过程控制和设备监控。在车型开发传统的可靠性研究使用传感器数据来监控设备的健康而对有价值的过程信息计数。相比之下，本研究提出，旨在提高生产系统的可靠性，使用来自操作控制和健康监测设备，它提供了可靠性的更稳健的模型数据的方法。为此，我们使用了半监督机器学习（ML）与卷积神经网络（CNN），自动编码器（AE）和袋装决策树来确定哪些变量是负责异常状况。通过这种方法，可以检测哪些变量是在故障发生最重要的是，使这增加了系统的可靠性预防措施。这种方法的主要贡献是分析技术整合，以提高系统的可靠性。测试和验证的方法，我们在苯乙烯石化厂进行真实数据的案例。其结果是，有可能找出失败的真空系统异常的开始。随着所提出的方法，缓解措施可以采取，因此，避免不必要的停机时间。