Reflectometry-based sensing systems are widely used in industrial monitoring to assess the condition of distributed assets such as cables and transmission lines. In practical sensing environments, however, correlation-based interpretation can become unreliable because of bilinear interference, dispersive propagation, and excitation mismatch, often producing artifact-related responses that lead to unnecessary inspections and reduced decision reliability. This paper proposes a temporal-consistency-based reliability enhancement framework for correlation-driven time–frequency domain reflectometry (TFDR). Instead of replacing the conventional reflectometry pipeline, the proposed method introduces a reliability-estimation layer that evaluates the trustworthiness of correlation responses and suppresses temporally inconsistent artifacts. Multiple complementary descriptors extracted from the reflected signal are jointly analyzed to determine whether a correlation response is propagation-consistent or more likely to arise from non-physical artifacts. Temporal consistency is modeled using a bidirectional long short-term memory (BiLSTM) architecture that captures long-range dependencies along the propagation sequence. Experimental results obtained from cable reflectometry measurements under varying impedance conditions show that the proposed framework effectively suppresses artifact-related correlation responses while preserving physically meaningful reflections required for fault localization. Additional cross-excitation evaluation provides preliminary evidence that the learned temporal-consistency criterion is not tightly coupled to a single excitation waveform. Because the proposed framework operates as a post-processing reliability layer, it can be integrated into existing reflectometry-based monitoring systems without the modification of the sensing hardware or excitation scheme.
Lee et al. (Sun,) studied this question.