面向不断演变运行工况的高效故障检测的连续测试时域自适应
Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions
AI总结：
本文提出TAAD框架，将输入分为系统参数与测量值并分别进行连续测试时域自适应，以解决工况演变和数据稀缺下的早期故障检测问题，在真实水泵数据上验证了其优越性。
摘要：
故障检测在工业系统中至关重要，它通过区分异常与正常运行工况来预防故障并优化性能。随着复杂工业系统状态监测数据量的增加，数据驱动方法在故障检测任务中日益受到欢迎。尽管取得了这些进展，在真实场景下早期故障检测仍然是一项挑战。运行工况和环境的高度可变性使得收集能够代表所有可能运行工况的全面训练数据集变得困难，尤其是在系统运行的早期阶段。此外，这些变化往往会随时间演变，可能导致未来出现全新的、此前未见过的数据分布。这些挑战阻碍了不同设备之间以及跨时间的直接知识迁移，导致训练数据与测试数据之间存在分布差距，并使这些方法在真实场景中的性能下降。为克服这一问题，我们的工作引入了一种新颖的连续测试时域自适应方法。该方法通过解决域偏移和有限数据代表性问题，实现了早期阶段的鲁棒异常检测。我们提出了一个测试时域自适应异常检测（TAAD）框架，该框架将输入变量分为系统参数和测量值两类，并采用两个域自适应模块分别对每一类输入进行独立自适应。该方法能够有效适应不断演变的运行工况，并且在数据稀缺的系统中尤其有益。我们的方法在一个真实水泵监测数据集上进行了测试，结果表明其在故障检测方面相比现有域自适应方法有显著提升，展现出更高的准确性和可靠性。
英文摘要：
Fault detection is crucial in industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. Data-driven methods have been gaining popularity for fault detection tasks as the amount of condition monitoring data from complex industrial systems increases. Despite these advances, early fault detection remains a challenge under real-world scenarios. The high variability of operating conditions and environments makes it difficult to collect comprehensive training datasets that can represent all possible operating conditions, especially in the early stages of system operation. Furthermore, these variations often evolve over time, potentially leading to entirely new data distributions in the future that were previously unseen. These challenges prevent direct knowledge transfer across different units and over time, leading to the distribution gap between training and testing data and inducing performance degradation of those methods in real-world scenarios. To overcome this, our work introduces a novel approach for continuous test-time domain adaptation. This enables early-stage robust anomaly detection by addressing domain shifts and limited data representativeness issues. We propose a Test-time domain Adaptation Anomaly Detection (TAAD) framework that separates input variables into system parameters and measurements, employing two domain adaptation modules to independently adapt to each input category. This method allows for effective adaptation to evolving operating conditions and is particularly beneficial in systems with scarce data. Our approach, tested on a real-world pump monitoring dataset, shows significant improvements over existing domain adaptation methods in fault detection, demonstrating enhanced accuracy and reliability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
