ABSTRACT

The capability to predict the behaviour of machines is nowadays experiencing a tremendous growth of interest within Industry 4.0-based manufacturing systems. The route to this end is not straightforward when Run-To-Failure (RTF) data are poorly available or not available at all, thus a strategy must be properly defined. In this proposal, assuming no RTF data, a novelty detection is combined with random coefficient statistical modelling for Remaining Useful Life (RUL) prediction. This approach is formalized by means of a reference framework extending the ISO 13374 – OSA-CBM standards. The framework guides the integration of novelty detection and RUL prediction finally implemented in the scope of a Flexible Manufacturing Line part of the Industry 4.0 Lab of the School of Management of Politecnico di Milano.

摘要

如今，在基于工业 4.0 的制造系统中，预测机器行为的能力正经历着巨大的增长。当 Run-To-Failure (RTF) 数据可用性较差或根本不可用时，实现这一目标的途径并不简单，因此必须正确定义策略。在这个提议中，假设没有 RTF 数据，新颖性检测与随机系数统计建模相结合，用于剩余使用寿命 (RUL) 预测。该方法通过扩展 ISO 13374 – OSA-CBM 标准的参考框架正式化。该框架指导了新颖性检测和 RUL 预测的集成，最终在米兰理工大学管理学院工业 4.0 实验室的柔性制造线部分范围内实施。