In-Situ Fault Detection of Submerged Pump Impellers Using Encapsulated Accelerometers and Machine Learning

Vertical turbine pumps in oil and gas operations rely on motor-mounted accelerometers for condition monitoring. However, these sensors cannot detect faults at submerged impellers exposed to harsh downhole environments. We present the first study deploying encapsulated accelerometers mounted directly on submerged impeller bowls, enabling in-situ vibration monitoring. Using a lab-scale pump setup with 1-meter oil submergence, we collected vibration data under normal and simulated fault conditions. The data were analyzed using a suite of machine learning models -- spanning traditional and deep learning methods -- to evaluate sensor effectiveness. Impeller-mounted sensors achieved 91.3% average accuracy and 0.973 AUC-ROC, outperforming the best non-submerged sensor. Crucially, encapsulation caused no statistically significant performance loss in sensor performance, confirming its viability for oil-submerged environments. While the lab setup used shallow submergence, real-world pump impellers operate up to hundreds of meters underground -- well beyond the range of surface-mounted sensors. This first-of-its-kind in-situ monitoring system demonstrates that impeller-mounted sensors -- encapsulated for protection while preserving diagnostic fidelity -- can reliably detect faults in critical submerged pump components. By capturing localized vibration signatures that are undetectable from surface-mounted sensors, this approach enables earlier fault detection, reduces unplanned downtime, and optimizes maintenance for downhole systems in oil and gas operations.

Closed-loop Feedback Registration for Consecutive Images of Moving Flexible Targets

Advancement of imaging techniques enables consecutive image sequences to be acquired for quality monitoring of manufacturing production lines. Registration for these image sequences is essential for in-line pattern inspection and metrology, e.g., in the printing process of flexible electronics. However, conventional image registration algorithms cannot produce accurate results when the images contain many similar and deformable patterns in the manufacturing process. Such a failure originates from a fact that the conventional algorithms only use the spatial and pixel intensity information for registration. Considering the nature of temporal continuity and consecution of the product images, in this paper, we propose a closed-loop feedback registration algorithm for matching and stitching the deformable printed patterns on a moving flexible substrate. The algorithm leverages the temporal and spatial relationships of the consecutive images and the continuity of the image sequence for fast, accurate, and robust point matching. Our experimental results show that our algorithm can find more matching point pairs with a lower root mean squared error (RMSE) compared to other state-of-the-art algorithms while offering significant improvements to running time.

Toward Real-world Image Super-resolution via Hardware-based Adaptive Degradation Models

Most single image super-resolution (SR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs, which are simulated by a predetermined degradation operation, e.g., bicubic downsampling. However, these methods only learn the inverse process of the predetermined operation, so they fail to super resolve the real-world LR images; the true formulation deviates from the predetermined operation. To address this problem, we propose a novel supervised method to simulate an unknown degradation process with the inclusion of the prior hardware knowledge of the imaging system. We design an adaptive blurring layer (ABL) in the supervised learning framework to estimate the target LR images. The hyperparameters of the ABL can be adjusted for different imaging hardware. The experiments on the real-world datasets validate that our degradation model can estimate LR images more accurately than the predetermined degradation operation, as well as facilitate existing SR methods to perform reconstructions on real-world LR images more accurately than the conventional approaches.