  This work describes an online processing pipeline designed to identify
anomalies in a continuous stream of data collected without external triggers
from a particle detector. The processing pipeline begins with a local
reconstruction algorithm, employing neural networks on an FPGA as its first
stage. Subsequent data preparation and anomaly detection stages are accelerated
using GPGPUs. As a practical demonstration of anomaly detection, we have
developed a data quality monitoring application using a cosmic muon detector.
Its primary objective is to detect deviations from the expected operational
conditions of the detector. This serves as a proof-of-concept for a system that
can be adapted for use in large particle physics experiments, enabling anomaly
detection on datasets with reduced bias.
