Anomaly detection and diagnosis in factory automation system using pre-processed time-delay neural network with loss function adaptation

A pre-processed time-delay autoencoder based anomaly detection system for detecting anomalous states of machines arranged in a factory automation (FA) system or a manufacturing production line, comprising steps of:

• an interface configured to acquire source signals from the machines;
• a memory to store computer-executable programs including a data pre-processing algorithm, a time-delayed data reform process algorithm, a pre-processed time-delay autoencoder (Pre-TDAE) neural network, and an anomaly diagnosis algorithm;
• and
• a processor configured to perform steps of:
• performing a data pre-processing process algorithm for the acquired source signals by normalizing value ranges of the acquired source signals and filtering undesired features from the acquired source signals;
• performing the time-delayed data reform process algorithm for the pre-processed source signals based on a time-delay window to generate pre-processed time-delay data;
• submitting pre-processed time-delay testing data to a Prep-TDAE neural network, wherein the pre-processed time-delay testing data are collected online while the machines are operated, wherein the Prep-TDAE neural network has been pre-trained by using pre-processed time-delay training data;
• detecting, if an anomaly state is encountered with respect to the machines, by computing anomaly scores of the pre-processed time-delay testing data;
• and
• determining, when the anomaly state is detected, anomaly occurrence time, duration and severity with respect to the anomaly state of each of the machines.