US 11,741,341 B2
Method and system for semi-supervised anomaly detection with feed-forward neural network for high-dimensional sensor data
Deokwoo Jung, Mountain View, CA (US)
Assigned to Palo Alto Research Center Incorporated, Palo Alto, CA (US)
Filed by Palo Alto Research Center Incorporated, Palo Alto, CA (US)
Filed on Oct. 4, 2019, as Appl. No. 16/593,248.
Prior Publication US 2021/0103794 A1, Apr. 8, 2021
Int. Cl. G06N 20/00 (2019.01); G06N 3/042 (2023.01); G06N 3/08 (2023.01); G06N 3/045 (2023.01); G06N 7/01 (2023.01)
CPC G06N 3/042 (2023.01) [G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01)] 14 Claims
OG exemplary drawing
 
1. A method for detecting an anomaly in operation of one or more machines, the method comprising:
obtaining a plurality of unlabeled sensor data samples from one or more sensors associated with the one or more machines;
training a plurality of randomly initialized clustering models in parallel using the unlabeled sensor data and user-provided partial label information including a set of normal labels to generate a set of estimated labels, wherein
training each randomly initialized clustering model comprises evaluating a reliability of the randomly initialized clustering model, wherein
evaluating the reliability of each randomly initialized clustering model comprises computing a weight associated with the randomly initialized clustering model based on a matching rate between estimated labels outputted by the randomly initialized clustering model and user-provided ground-truth labels, and wherein
the estimated labels are determined by calculating

OG Complex Work Unit Math
with x* being the unlabeled sensor data;
computing, for each unlabeled sensor data sample, an abnormal probability based on the estimated labels and the weight associated with each randomly initialized clustering model;
applying a random sample generator to generate multiple sets of labeled training samples based on the abnormal probability;
training a set of feed-forward neural network (FNN) models in parallel, wherein a respective FNN model of the set of trained FNN models is trained using a corresponding set of labeled training samples;
obtaining, for an observed sensor data sample, a set of predicted labels outputted by the set of trained FNN models, wherein each trained FNN model outputs a predicted label;
computing an average of the set of predicted labels outputted by the set of trained FNN models; and
determining whether an anomaly is present in the operation of the one or more machines based on whether the average of the set of predicted labels is greater than a user-specified threshold.