公开(公告)号：US20240362463A1

公开(公告)日：2024-10-31

申请号：US18692629

申请日：2022-09-15

Applicant: BAE SYSTEMS Plc

Inventor： Samuel Horry ,  Richard Edward John Nicklin ,  Hans-Heinrich Mai

IPC: G06N3/0455 ,  G06F11/07

CPC classification number: G06N3/0455 ,  G06F11/0721 ,  G06F11/079

Abstract: The invention relates to a system and method for detecting anomalous system behaviour. The system comprises a plurality of sensors and a trained autoencoder. The method of training comprises: obtaining training data and test data comprising multiple data records for at least one engineering asset which corresponds to the engineering asset whose behaviour is to be classified, wherein the data records comprise a plurality of sensor readings for the engineering asset; fitting the autoencoder to the obtained training data; running the test data through the encoder of the fitted autoencoder to obtain encodings of the test data; generating a plurality of data sets from the obtained encodings, wherein the generated plurality of data sets include under-represented data sets; cloning the fitted autoencoder to create a cloned autoencoder for each of the generated plurality of data sets; and aggregating the cloned autoencoders to form an over-arching autoencoder. The method further comprises calculating an error data set between the training data and data reconstructed by the over-arching auto encoder; obtaining, using the calculated error data set, estimated parameters for calculating an anomaly score for each data record, wherein the anomaly score is selected from a Mahalanobis distance and a squared Mahalanobis distance; and estimating, using the calculated error set, parameters for calculating a decomposition of the anomaly score to identify a contribution from each sensor reading to the anomaly score.

公开(公告)号：US20240427320A1

公开(公告)日：2024-12-26

申请号：US18705118

申请日：2022-10-27

Applicant: BAE SYSTEMS plc

Inventor： Samuel Horry ,  Hans-Heinrich Mai ,  Richard Edward John Nicklin

IPC: G05B23/02 ,  G06N7/01

Abstract: A method and apparatus for predicting failure of an engineering asset based on real-time data. The system comprises a plurality of sensors for measuring data on the engineering asset. The method of predicting failure comprises receiving a data record comprising data on the engineering asset collected from the plurality of sensors at time t, generating, using a trained machine learning algorithm, a probability PF that the received data record indicates that the engineering asset is in a faulty state; determining what number of data records received in a look-back time Lt are indicative of the engineering asset being in a faulty state, wherein the look-back time Lt is a time period occurring before the time t at which the data record was collected; and predicting a probability of the engineering asset failing during a horizon time Ht, wherein the horizon time Ht is a time period after time t at which the data record was collected. The predicting step implements a Bayes forecasting model to predict the probability of failure based on the generated probability PF and the number of data records which were determined to be faulty within the look-back time Lt.