US 12,007,760 B2
Anomaly detection and diagnosis in factory automation system using pre-processed time-delay neural network with loss function adaptation
Jianlin Guo, Newton, MA (US); Bryan Liu, Sydney (AU); Toshiaki Koike Akino, Belmont, MA (US); Ye Wang, Andover, MA (US); Kyeong Jin Kim, Lexington, MA (US); Kieran Parsons, Gloucester, MA (US); and Philip Orlik, Cambridge, MA (US)
Assigned to Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US)
Filed by Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US)
Filed on Sep. 2, 2021, as Appl. No. 17/464,901.
Prior Publication US 2023/0068908 A1, Mar. 2, 2023
Int. Cl. G05B 23/02 (2006.01); G05B 19/418 (2006.01); G06F 18/214 (2023.01); G06N 3/049 (2023.01)
CPC G05B 23/0283 (2013.01) [G05B 19/4184 (2013.01); G06F 18/2148 (2023.01); G06N 3/049 (2013.01); G05B 23/0221 (2013.01)] 12 Claims
OG exemplary drawing
 
1. A computer-implemented pre-processed time-delay autoencoder based anomaly detection method for detecting anomalous states of machines arranged in a factory automation (FA) system or a manufacturing production line, comprising steps of:
acquiring source signals from the machines via an interface;
performing a data pre-processing process for the acquired source signals by normalizing value ranges of the acquired source signals and filtering undesired features from the acquired source signals;
performing a time-delayed data reform process 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 pre-processed time-delayed autoencoder (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 a 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.