Architecture for anomaly detection in a laser heating surface process

Abstract

Anomaly detection is an increasingly common task in many industrial environments. Cyber-physical systems stand out in this field due to their unique position in industrial areas. This paper introduces a new architecture aimed to detect anomalies in a real laser heating surface process, which is designed for field-programmable gate arrays (FPGAs). The FPGA design offers advantages of highly parallelized and pipelined architectures. The system will classify one process into normal or abnormal taking into account spatial information about where the laser spot is. The proposed design estimates a probability density function from data; then it performs an image convolution transforming the probability density function into a kernel density estimation function. This estimated function should be able to classify in real time.