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1.
公开(公告)号:US11781943B2
公开(公告)日:2023-10-10
申请号:US16864208
申请日:2020-05-01
Applicant: ABB Schweiz AG
Inventor: Jari Jäppinen , Olli Liukkonen , Timo Holopainen , Markku Niemelä , Ville Särkimäki
CPC classification number: G01M13/045 , F16C19/02 , G01D21/02 , G01J5/02 , G01M13/00 , G01N27/22 , G01P3/36 , F16C2233/00
Abstract: An arrangement for monitoring an antifriction bearing of a rotating shaft of a rotating electric machine. The arrangement includes: one or more capacitor electrodes to measure a capacitive shaft displacement parameter; one or more of the following additional measurement sensors; a microphone to measure a bearing noise parameter, a voltage sensor to measure a bearing current parameter, and/or an optical pyrometer to measure a shaft heat parameter; and one or more processors configured to evaluate a condition of the antifriction bearing based on the capacitive shaft displacement parameter and one or more of the following: the bearing noise parameter, the bearing current parameter, and/or the shaft heat parameter.
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公开(公告)号:US11853046B2
公开(公告)日:2023-12-26
申请号:US17772553
申请日:2020-10-15
Applicant: ABB Schweiz AG
Inventor: Olli Alkkiomáki , Joni Siimesjärvi , Ville Särkimäki
IPC: G05B23/02
CPC classification number: G05B23/0229 , G05B23/024 , G05B23/0283
Abstract: Disclosed herein is a method for predicting a faulty behaviour of an electrical converter. The method includes receiving an operation point indicator of the electrical converter indicative of an actual operation point of the electrical converter, where the electrical converter is connected to a rotating electrical machine; receiving a measured device temperature of a power semiconductor device of the electrical converter indicative of an actual temperature of the power semiconductor device; inputting the operation point indicator as input data into a machine learning algorithm trained with historical data comprising operation point indicators and associated device temperatures, where the historical data was recorded during normal operation of a power semiconductor device; estimating an estimated device temperature with the machine learning algorithm, where the estimated device temperature represents a device temperature during a normal operation; and predicting the faulty behaviour by comparing the estimated device temperature with the measured device temperature.
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3.
公开(公告)号:US20200256763A1
公开(公告)日:2020-08-13
申请号:US16864208
申请日:2020-05-01
Applicant: ABB Schweiz AG
Inventor: Jari Jäppinen , Olli Liukkonen , Timo Holopainen , Markku Niemelä , Ville Särkimäki
IPC: G01M13/045 , F16C19/02
Abstract: An arrangement for monitoring an antifriction bearing of a rotating shaft of a rotating electric machine. The arrangement includes: one or more capacitor electrodes to measure a capacitive shaft displacement parameter; one or more of the following additional measurement sensors; a microphone to measure a bearing noise parameter, a voltage sensor to measure a bearing current parameter, and/or an optical pyrometer to measure a shaft heat parameter; and one or more processors configured to evaluate a condition of the antifriction bearing based on the capacitive shaft displacement parameter and one or more of the following: the bearing noise parameter, the bearing current parameter, and/or the shaft heat parameter.
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公开(公告)号:US20220397883A1
公开(公告)日:2022-12-15
申请号:US17806195
申请日:2022-06-09
Applicant: ABB Schweiz AG
Inventor: Ville Särkimäki , Zhongliang Hu
IPC: G05B19/406
Abstract: To provide adaptiveness to data collected on an industrial automation device, a test is used for a specific need. A set of parameter values to be used during the test are temporary and send to the industrial automation device with an indication of a test. Before the test is run, existing parameter values are backed up, and they are restored after the test.
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公开(公告)号:US20220382269A1
公开(公告)日:2022-12-01
申请号:US17772553
申请日:2020-10-15
Applicant: ABB Schweiz AG
Inventor: Olli Alkkiomäki , Joni Siimesjärvi , Ville Särkimäki
IPC: G05B23/02
Abstract: Disclosed herein is a method for predicting a faulty behaviour of an electrical converter. The method includes receiving an operation point indicator of the electrical converter indicative of an actual operation point of the electrical converter, where the electrical converter is connected to a rotating electrical machine; receiving a measured device temperature of a power semiconductor device of the electrical converter indicative of an actual temperature of the power semiconductor device; inputting the operation point indicator as input data into a machine learning algorithm trained with historical data comprising operation point indicators and associated device temperatures, where the historical data was recorded during normal operation of a power semiconductor device; estimating an estimated device temperature with the machine learning algorithm, where the estimated device temperature represents a device temperature during a normal operation; and predicting the faulty behaviour by comparing the estimated device temperature with the measured device temperature.
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