Fault diagnosis of a thermal system under varying operating conditions is of great importance for the safe and reliable operation of a power plant involved in peak shaving. However, it is a difficult task due to the lack of sufficient labeled data under some operating conditions. In practical applications, the model built on the labeled data under one operating condition will be extended to such operating conditions. Data distribution discrepancy can be triggered by variation of operating conditions and may degenerate the performance of the model. Considering the fact that data distributions are different but related under different operating conditions, this paper proposes a hierarchical deep domain adaptation (HDDA) approach to transfer a classifier trained on labeled data under one loading condition to identify faults with unlabeled data under another loading condition. In HDDA, a hierarchical structure is developed to reveal the effective information for final diagnosis by layerwisely capturing representative features. HDDA learns domain-invariant and discriminative features with the hierarchical structure by reducing distribution discrepancy and preserving discriminative information hidden in raw process data. For practical applications, the Taguchi method is used to obtain the optimized model parameters. Experimental results and comprehensive comparison analysis demonstrate its superiority.

中文翻译：

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电厂热力系统故障诊断的分层深域自适应方法


不同工况下热力系统的故障诊断对于调峰电厂的安全可靠运行具有重要意义。然而，由于在某些操作条件下缺乏足够的标记数据，这是一项艰巨的任务。在实际应用中，基于一种工况下的标记数据建立的模型将扩展到这样的工况。操作条件的变化可能会触发数据分布差异，并可能降低模型的性能。考虑到数据分布在不同操作条件下不同但相关的事实，本文提出了一种分层深度域适应（HDDA）方法，将在一种负载条件下对标记数据训练的分类器转移到另一种负载条件下识别未标记数据的故障。在HDDA中，开发了一种层次结构，通过分层捕获代表性特征来揭示最终诊断的有效信息。 HDDA 通过减少分布差异并保留隐藏在原始过程数据中的判别信息，学习具有层次结构的域不变和判别特征。实际应用中，采用田口法来获得优化的模型参数。实验结果和综合比较分析证明了其优越性。