Diffusion Partition Consensus: Diffusion-Aided Time-of-Flight Estimates, Anomaly Detection, and Localization for Ultrasonic Nondestructive Evaluation Data
Diffusion partition consensus is a novel generative AI-based technique for time-series anomaly detection and data imputation in the presence of outliers. To illustrate the method, an implementation with design choices tailored for well-structured time series typical of single probe ultrasonic nondes...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Open Journal of Instrumentation and Measurement |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10734664/ |
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| Summary: | Diffusion partition consensus is a novel generative AI-based technique for time-series anomaly detection and data imputation in the presence of outliers. To illustrate the method, an implementation with design choices tailored for well-structured time series typical of single probe ultrasonic nondestructive evaluation (NDE) datasets is proposed. The technique relies on cross-talk between a conditional score-based diffusion model, and two well-chosen Savitzky-Golay filters. Testing and evaluation are performed on a series of progressively information rich synthetic datasets, and on real-world ultrasonic NDE datasets taken from a Canada Deuterium Uranium nuclear reactor pressure tube and calibration fixture. The iterative technique is a blend of stochastic and deterministic methods that uses confidence and consensus of target parameter estimates to update several data classifying partitions over the dataset, which in turn allows a new set of estimates and confidence measures to be established. Data classification induces a progressive bias in the training datasets allowing a diffusion model to identify the prevalent distribution. Methods for fault diagnosis support the efficacious inclusion of a human in the loop making the technique suitable for use in safety-critical applications. The main advantages of the technique are that it is unsupervised—in that it does not require labeled datasets or significant data preprocessing, does not rely on out-of-distribution generalization, provides means for fault diagnosis without recourse to ground truth, converges with stability, and naturally includes a human in the loop. The quality of results, the checks and balances provided by the fault diagnosis mechanism, and the opportunity to include a human in the loop, support the case for usage in safety-critical contexts such as NDE at a nuclear power facility. |
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| ISSN: | 2768-7236 |