Scholarlink

<p>Anomaly detection in industries is critical to ensure safety, reliability, and product quality. Industrial processes often produce high-dimensional multivariate time series (MVTS) data from sensors and actuators, making the detection of subtle or complex anomalies increasingly challenging. In this work, we introduce the novel application of curriculum learning (CL) to deep MVTS anomaly detection, a strategy inspired by the human learning process that begins training with easier examples before progressing to more difficult ones. We propose two CL frameworks: a <i>data-based curriculum</i>, which ranks training samples by signal complexity (e.g. noise level, window size), and a <i>model-based curriculum</i>, in which the knowledge learned from a simple long short-term memory encoder-decoder model is used to initialize a more advanced multi-scale convolutional recurrent encoder-decoder model. Furthermore, we present the first use of <i>system signature matrices</i> as an MVTS representation that captures spatial-temporal dependencies in textile process data for collective anomaly detection. We perform comprehensive evaluations on the SWaT benchmark dataset and a dataset collected from real-world textile processes. Our empirical results demonstrate the possibility of designing curriculum-trained models that outperform standard training baselines, achieving higher F1 scores while offering more structured training dynamics. Notably, the data-based CL strategy consistently yields better performance than non-curriculum baselines. This study represents the first systematic adaptation of CL to industrial anomaly detection and establishes a foundation for more structured training paradigms in MVTS anomaly detection.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Industrial anomaly detection via curriculum-based deep learning

  • 期刊论文

  • 发表日期：

    2025年12月9日

查看全文
  • Devilliers Dube,
  • Mehmet Akar

摘要

Anomaly detection in industries is critical to ensure safety, reliability, and product quality. Industrial processes often produce high-dimensional multivariate time series (MVTS) data from sensors and actuators, making the detection of subtle or complex anomalies increasingly challenging. In this work, we introduce the novel application of curriculum learning (CL) to deep MVTS anomaly detection, a strategy inspired by the human learning process that begins training with easier examples before progressing to more difficult ones. We propose two CL frameworks: a data-based curriculum, which ranks training samples by signal complexity (e.g. noise level, window size), and a model-based curriculum, in which the knowledge learned from a simple long short-term memory encoder-decoder model is used to initialize a more advanced multi-scale convolutional recurrent encoder-decoder model. Furthermore, we present the first use of system signature matrices as an MVTS representation that captures spatial-temporal dependencies in textile process data for collective anomaly detection. We perform comprehensive evaluations on the SWaT benchmark dataset and a dataset collected from real-world textile processes. Our empirical results demonstrate the possibility of designing curriculum-trained models that outperform standard training baselines, achieving higher F1 scores while offering more structured training dynamics. Notably, the data-based CL strategy consistently yields better performance than non-curriculum baselines. This study represents the first systematic adaptation of CL to industrial anomaly detection and establishes a foundation for more structured training paradigms in MVTS anomaly detection.