Performance analysis of the existing mechanical products is critical to identifying design defects and improving product reliability. With the advances of information technologies, product operating data collected through continuous condition monitoring (CM) serve as main sources for analysis of performance and detection of anomaly. Most of the existing anomaly detection methods, however, are not effective when CM data are very high dimensional, leading to poor quality of assessment results. Besides, the effects of multiple operating conditions on anomaly detection are seldom considered in these existing methods. To solve these problems, an integrated approach for anomaly detection and critical behavioral attributes identification based on CM data is developed in this research. Gaussian mixed model GMM) is employed to develop a method for clustering of operating conditions. Isolation forest (iForest) method is used to detect anomaly instances, and further to identify the critical attributes related to product performance degradation. The effectiveness of the developed approach is demonstrated by an application with collected operating data of a wind turbine.

现有机械产品的性能分析对于识别设计缺陷和提高产品可靠性至关重要。随着信息技术的发展，通过连续状态监视（CM）收集的产品运行数据成为分析性能和检测异常的主要来源。但是，当CM数据的维数很高时，大多数现有的异常检测方法均无效，从而导致评估结果的质量较差。此外，在这些现有方法中很少考虑多种操作条件对异常检测的影响。为了解决这些问题，本研究开发了一种基于CM数据的异常检测和关键行为属性识别的集成方法。高斯混合模型（GMM）用于开发一种对运行条件进行聚类的方法。隔离林（i Forest）方法用于检测异常实例，并进一步确定与产品性能下降有关的关键属性。所开发的方法的有效性通过具有风力涡轮机运行数据收集的应用程序得到证明。