Anomaly Detection and Localization for Images of Running Paper Web in Paper Manufacturing

We introduce a new method based on convolutional autoencoders to detect and locate paper web anomalies that can cause web breaks during the paper production process. In this approach, we pre-process the images, captured by two high-speed cameras located at the opposite sides of the running paper web at a paper machine, in several steps to remove noises and separate the paper web areas from the background. After designing and training a convolutional autoencoder with non-anomalous samples, a novel anomaly score and map are defined to find and locate web irregularities based on an edge detector and a reconstruction error, defined by the combination of absolute error and Structural Similarity Index Measure between the reconstructed and the original images, in each test sample. By assessing the proposed approach on the images taken from a real paper machine, we discover that this method can detect paper defects properly and, therefore it has the potential to improve machine functionality and even to prevent certain types of web breaks, which reduces the machine downtime, paper losses, maintenance costs, and energy consumption, i.e., increases the performance and efficiency of paper machinery.