公开(公告)号：US20220004163A1

公开(公告)日：2022-01-06

申请号：US17480165

申请日：2021-09-21

Applicant: ABB Schweiz AG

Inventor： Ido Amihai ,  Subanatarajan Subbiah ,  Arzam Muzaffar Kotriwala ,  Moncef Chioua

IPC: G05B19/4065 ,  G05B23/02 ,  G06N3/04 ,  G06K9/62

Abstract: An apparatus includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with sensor data for an item of equipment. The processing unit is configured to implement at least one machine learning algorithm, which has been trained on the basis of a plurality of calibration sensor data for the item of equipment. Training of the at least one machine learning algorithm includes processing the plurality of calibration sensor data to determine at least two clusters representative of different equipment states. The processing unit is configured to implement the at least one machine learning algorithm to process the sensor data to assign the sensor data to a cluster of the at least two clusters to determine an equipment state for the item of equipment. The output unit is configured to output the equipment state for the item of equipment.

公开(公告)号：US10969774B2

公开(公告)日：2021-04-06

申请号：US16576832

申请日：2019-09-20

Applicant: ABB Schweiz AG

Inventor： Martin Hollender ,  Benjamin Kloepper ,  Michael Lundh ,  Moncef Chioua

IPC: G06G7/00 ,  G05B23/02 ,  G05B13/04 ,  G06N20/10

Abstract: An anomaly detection module is configured to apply a plurality of machine learning models to received technical status data to detect one or more indicators of an abnormal technical status prevailing in the industrial process system. The plurality of machine learning models are trained on historic raw or pre-processed sensor data and the anomaly detection module configured to generate the anomaly alert based on the one or more indicators. The received technical status data is assigned to signal groups and the generated anomaly alert is a vector with each vector element representing a group anomaly indicator for the respective signal group. Each vector element is determined by applying a respective group specific machine learning model.

公开(公告)号：US20180087489A1

公开(公告)日：2018-03-29

申请号：US15828450

申请日：2017-12-01

Applicant: ABB Schweiz AG

Inventor： Moncef Chioua ,  Ni Ya Chen ,  RongRong Yu ,  Yingya Zhou ,  Yao Chen

IPC: F03D17/00 ,  G06F11/30

CPC classification number: F03D17/00 ,  F05B2240/96 ,  G06F11/30 ,  Y02B10/30

Abstract: A method for monitoring turbines of a windmill farm includes: providing a global nominal dataset containing frame data of the turbines of the windmill farm and continuous reference monitoring data of the turbines for a first period in a fault free state, the reference monitoring data including at least two same monitoring variables for each turbine; building a nominal global model based on the global nominal dataset which describes the relationship in between the windmill turbines and clustering the turbines according thereto; assigning the data of the global nominal dataset to respective nominal local datasets according to the clustering; and building a nominal local model for the turbines of each cluster based on the respective assigned nominal local datasets, the nominal local model being built such that a nonconformity index is provideable which indicates a degree of nonconformity between data projected on the local model and the model itself.