Complex industrial systems are continuously monitored by a large number of heterogeneous sensors. The diversity of their operating conditions and the possible fault types make it impossible to collect enough data for learning all the possible fault patterns. The paper proposes an integrated automatic unsupervised feature learning and one-class classification for fault detection that uses data on healthy conditions only for its training. The approach is based on stacked Extreme Learning Machines (namely Hierarchical, or HELM) and comprises an autoencoder, performing unsupervised feature learning, stacked with a one-class classifier monitoring the distance of the test data to the training healthy class, thereby assessing the health of the system. This study provides a comprehensive evaluation of HELM fault detection capability compared to other machine learning approaches, such as stand-alone one-class classifiers (ELM and SVM), these same one-class classifiers combined with traditional dimensionality reduction methods (PCA) and a Deep Belief Network. The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data. Subsequently, the approach is evaluated on a real case study of a power plant fault. The proposed algorithm for fault detection, combining feature learning with the one-class classifier, demonstrates a better performance, particularly in cases where condition monitoring data contain several non-informative signals.
翻译:由于操作条件和可能的故障类型多种多样,因此无法收集足够的数据,以了解所有可能的故障模式。本文件建议采用综合自动不受监督的特征学习和故障检测分类,仅将健康条件数据用于培训,采用关于健康条件的数据,对故障检测进行综合自动自动、无监督的特征学习和单级分类;该方法以堆叠式极端学习机(即等级式或高频系统)为基础,由自动编码器组成,进行不受监督的特征学习,并堆叠成一个单级分类器,监测测试数据与培训健康班的距离,从而评估系统的健康状况。随后,该方法提供了与其他机器学习方法相比,如单级单级分类器(ELM和SVM),对HELM缺陷检测能力的综合评价,这些单级分类器与传统的减少维度方法(PCA)和深信仰网络相结合。首先对包含典型状况监测数据的合成数据集进行了评估,随后,在对电源厂错误的真实案例研究上对这种方法进行了评估,与其他机器学习方法(例如独立单级单级单级级分类和SVM)相比,这些单级分类法与一种更好的业绩检测模型。