Fault Diagnosis and Region Detection of CCD Image in Double Delay Three-Step Autocorrelator

With the development of national optical experimental facilities, countries worldwide have invested significant amounts of funding into constructing various facilities. Among them, the double delay three-step autocorrelator is an important instrument for measuring the ultrashort pulse laser waveform and is frequently used in laser experiments. Because electronic instruments are too slow to measure directly, we can only use CCD images to reconstruct the laser waveform. Therefore, CCD, in particular, plays a vital role in the closed-loop control system of double delay three-step autocorrelator, and CCD image quality and processing can directly affect the measurement results of the pulse laser. In this paper, we propose an image feature-based CCD fault diagnosis and effective region detection method for the double delay three-step autocorrelator. This fault diagnosis method first calculates the pixel sum of each row and analyzes the primary pixel distribution of each frame which could represents the main features of CCD image. Next, we design a fault diagnosis model to classify those features and pick out the suitable image. Finally, we analyze and process the above image and design a boundary detection algorithm to detect the effective region for further waveform recovery algorithm. In addition, to verify performance of the proposed method, we conduct a significant number of experiments. Through qualitative and quantitative analyses of the experimental results, we verify the effectiveness and robustness of the proposed method for CCD fault diagnosis.