Machine Vision Based On-Board Diagnostics Model to Characterize Surface Degradation of Turbomachinery Components

Abstract This article proposes a novel machine vision (MV)-based technique to assess surface roughness (Ra) as an online diagnostic and monitoring system tool. Such a tool helps reduce operational downtime and minimize human intervention. The proposed method involves image acquisition in a controlled environment, detection of surface features through a parameter called “edge frequency” using the edge detection algorithms (EDAs), and developing a correlation between the estimated edge frequency and the experimentally measured Ra. Several well-known EDAs are evaluated on different samples, including emery papers with various grit values and turbomachinery blades. The superiority of Laplacian of Gaussian (LoG)-based EDA in terms of its resilience to noise and computational benefit is demonstrated. The edge frequency–Ra correlation is subsequently used to predict the Ra value of different sets to demonstrate prediction accuracy. Compared to contact-based measurements, the predictions on the emery samples are within 4.8%, while those on the blade samples are within 5.9%. Finally, the established correlation is used for the online diagnosis of a legacy axial compressor under cleaned and fouled conditions. The predicted Ra values for the samples and the trends agree with the historical data reported by the original equipment manufacturers (OEMs), demonstrating its ability as an on-board diagnostic (OBD) tool.