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Detecting abnormal sources within distributed parameter systems is a type of inverse source problem. Despite its industrial importance, studies on this topic remain scarce. This chapter presents the first effort to identify spatio-temporal anomalies in a particular class of distributed parameter systems. A new inverse spatio-temporal model is introduced, including an adaptive state observer for source detection and an adaptive estimation algorithm. One main advantage is that the method requires only system outputs, without direct measurement of system states. Theoretical analysis confirms that the estimation error converges. Finally, the approach is validated on a heat transfer rod containing an abnormal spatio-temporal source.

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Inverse S-T Model-Based Abnormal Source Identification for Parabolic DPSs

  • 章节

  • 发表日期：

    2026年

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  • Yun Feng,
  • Han-Xiong Li,
  • Yaonan Wang

摘要

Detecting abnormal sources within distributed parameter systems is a type of inverse source problem. Despite its industrial importance, studies on this topic remain scarce. This chapter presents the first effort to identify spatio-temporal anomalies in a particular class of distributed parameter systems. A new inverse spatio-temporal model is introduced, including an adaptive state observer for source detection and an adaptive estimation algorithm. One main advantage is that the method requires only system outputs, without direct measurement of system states. Theoretical analysis confirms that the estimation error converges. Finally, the approach is validated on a heat transfer rod containing an abnormal spatio-temporal source.