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1. 发明申请

WO2022074869A1 SYSTEM AND METHOD FOR PRODUCING METADATA OF AN AUDIO SIGNAL 审中-公开
公开(公告)号：WO2022074869A1
公开(公告)日：2022-04-14
申请号：PCT/JP2021/017609
申请日：2021-04-27
申请人： MITSUBISHI ELECTRIC CORPORATION
发明人： MORITZ, Niko , WICHERN, Gordon , HORI, Takaaki , LE ROUX, Jonathan
IPC分类号： G10L15/16 , G10L25/30 , G10L25/51 , G10L25/78 , G10L15/06 , G10L15/04 , G05B23/02 , G05B19/418 , G05B23/024 , G10L15/063 , G10L15/26
摘要： An audio processing system is provided. The audio processing system comprises an input interface configured to accept an audio signal. Further, the audio processing system comprises a memory configured to store a neural network trained to determine different types of attributes of multiple concurrent audio events of different origins, wherein the types of attributes include time-dependent and time-agnostic attributes of speech and non-speech audio events. Further, the audio processing system comprises a processor configured to process the audio signal with the neural network to produce metadata of the audio signal, the metadata including one or multiple attributes of one or multiple audio events in the audio signal.

2. 发明申请

WO2022002977A1 VERFAHREN UND SYSTEM ZUR FEHLERURSACHENANALYSE IN EINER PROZESSTECHNISCHEN ANLAGE 审中-公开
公开(公告)号：WO2022002977A1
公开(公告)日：2022-01-06
申请号：PCT/EP2021/067918
申请日：2021-06-29
申请人： SIEMENS AKTIENGESELLSCHAFT
发明人： BIERWEILER, Thomas , LABISCH, Daniel , SYLA, Erion
IPC分类号： G05B23/02 , G05B23/024 , G05B23/0254
摘要： Zur Fehlerursachenanalyse in einer prozesstechnischen Anlage (1), werden Engineering-Informationen (18) der Anlage (1), welche Informationen über die Anlagenkomponenten und ihre Verschaltung in der Anlage (1) beinhalten, in digitaler Form bereitgestellt, um aus den Engineering-Informationen (18) ein Inferenzmodell (30) in Form eines probabilistischen physikalischen Modells der Anlage (1) mit Wahrscheinlichkeitsverteilungen und Priorvariablen zu erstellen. In einem Diagnosemodus (40) des Inferenzmodells (30) wird unter Verwendung von Messdaten aus der Anlage (1) eine Bayes'sche Inferenz von Fehlerwahrscheinlichkeiten durchgeführt.

3. 发明申请

WO2021249115A1 GENERATING A HYBRID SENSOR TO COMPENSATE FOR INTRUSIVE SAMPLING 审中-公开
公开(公告)号：WO2021249115A1
公开(公告)日：2021-12-16
申请号：PCT/CN2021/093871
申请日：2021-05-14
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION , IBM (CHINA) CO., LIMITED
发明人： ZHOU, Nianjun , SUBRAMANIAN, Dharmashankar , GIFFORD, Wesley M.
IPC分类号： G06N3/04 , G05B13/027 , G05B23/024 , G05B23/0297 , G06Q50/04
摘要： A hybrid sensor can be generated by training a machine learning model, such as a neural network, based on a training data set. The training data set can include a first time series of upstream sensor data (504, 514, 702, 802) having forward dependence to a target variable (108, 110, 112, 604, 708, 804), a second time series of downstream sensor data (508, 518, 704, 806) having backward dependence to the target variable (108, 110, 112, 604, 708, 804) and a time series of measured target variable (108, 110, 112, 604, 708, 804) data associated with the target variable (108, 110, 112, 604, 708, 804). The target variable (108, 110, 112, 604, 708, 804) has measuring frequency which is lower than the measuring frequencies associated with the upstream sensor data (504, 514, 70, 802) and the downstream sensor data (508, 518, 704, 806). The hybrid sensor can estimate a value of the target variable (108, 110, 112, 604, 708, 804) at a given time, for example, during which no actual measured target variable (108, 110, 112, 604, 708, 804) value is available.

4. 发明申请

WO2022207653A1 OPERATOR CENTRIC SYSTEM FOR RESOLUTION OF ABNORMAL SITUATIONS IN A PLANT 审中-公开
公开(公告)号：WO2022207653A1
公开(公告)日：2022-10-06
申请号：PCT/EP2022/058302
申请日：2022-03-29
申请人： ABB SCHWEIZ AG
发明人： ZIOBRO, Dawid , KOTRIWALA, Arzam Muzaffar , GAERTLER, Marco , DOPPELHAMER, Jens , RODRIGUEZ, Pablo , BERNING, Matthias , KLOEPPER, Benjamin , BORRISON, Reuben , DIX, Marcel , SCHMIDT, Benedikt , ABUKWAIK, Hadil , MACZEY, Sylvia , LINGE, Simon , SHARMA, Divyasheel , K R, Chandrika , GOPALAKRISHNAN, Gayathri
IPC分类号： G05B23/02 , G05B23/024
摘要： The invention relates to the field of industrial plants and/or industrial processes, particularly to a system for diagnosis and assistance in the plants, wherein the plants comprise components of the plant. The assistance system (100) comprises a plant topology repository (110), comprising a representation of the components of the plant and relations between the components; a monitoring subsystem (120), configured for monitoring signals from the components and for monitoring a related event, as a key for the monitored signals; an aggregation subsystem (130), configured for storing a plurality of the monitored signals and the related events, wherein at least one of the events is the abnormal situation; an identification subsystem (140), configured for comparing currently monitored signals to stored monitored signals and the related event; and an evaluation subsystem (150), configured for outputting a predefined action, if the currently monitored signals match to the event that is the abnormal situation.

5. 发明申请

WO2022006077A8 MACHINE LEARNING BASED DIRECT METHOD OF DETERMINING STATUS OF FACILITY CONTROL LOOP COMPONENTS 审中-公开
公开(公告)号：WO2022006077A8
公开(公告)日：2022-01-06
申请号：PCT/US2021/039566
申请日：2021-06-29
申请人： HONEYWELL INTERNATIONAL INC.
发明人： DAVE, Sanjay Kantilal , JAIN, Akanksha , SRIVASTAVA, Viraj , AKAVALAPPIL, Vijoy
IPC分类号： G05B23/02 , G06N3/08 , G05B23/024 , G05B23/0272 , G06N3/049
摘要： A trained machine learning algorithm processes time series production data. The time series production data are representative of a control process within a facility control loop. The machine learning training algorithm is trained using positive training data that are representative of a normal operation of components within the facility control loop and negative training data that are representative of an abnormal operation of components within the facility control loop. Output of the trained machine learning algorithm identifies abnormalities in the facility control loop.

6. 发明申请

WO2021197783A1 TRAINING AN ARTIFICIAL INTELLIGENCE MODULE FOR INDUSTRIAL APPLICATIONS 审中-公开
公开(公告)号：WO2021197783A1
公开(公告)日：2021-10-07
申请号：PCT/EP2021/056099
申请日：2021-03-10
申请人： ABB SCHWEIZ AG
发明人： SCHMIDT, Benedikt , AMIHAI, Ido , KOTRIWALA, Arzam , CHIOUA, Moncef , LENDERS, Felix , JANKA, Dennis , HOLLENDER, Martin , SCHLAKE, Jan Christoph , ABUKWAIK, Hadil , KLOEPPER, Benjamin
IPC分类号： G06N20/00 , G06N3/08 , G06N3/04 , G05B23/02 , G05B19/418 , G05B23/024 , G06N3/0454 , G06N3/0472
摘要： A computer-implemented method of generating a training data set for training an artificial intelligence module (10), AI module, is provided. The method comprises providing, on a data storage (102), a first data set (12) and a second data set (14), wherein the first data set 5 includes one or more first data elements (13) indicative of a first operational condition of an industrial system, wherein the second data set includes one or more second data elements (15) indicative of a second operational condition of the industrial system, wherein the first operational condition substantially matches the second operational condition. The method further comprises determining a data transformation for transforming the one or more first 10 data elements (13) of the first data set (12) into the one or more second data elements (15) of the second data set (14), applying the determined data transformation to the one or more first data elements (13) of the first data set and/or to one or more further data elements of one or more further data sets, thereby generating a transformed data set, and generating a training data set for training the AI module (10) based on at least a part of the transformed 15 data set.

7. 发明申请

WO2021191190A1 VERFAHREN UND SYSTEM ZUR DIAGNOSE VON MELDUNGEN 审中-公开
公开(公告)号：WO2021191190A1
公开(公告)日：2021-09-30
申请号：PCT/EP2021/057393
申请日：2021-03-23
申请人： SIEMENS AKTIENGESELLSCHAFT
发明人： BIERWEILER, Thomas , JOHN, Jean Pascal , LABISCH, Daniel
IPC分类号： G05B23/02 , G06N3/08 , G05B2219/21002 , G05B2219/25255 , G05B23/024 , G06N20/00 , G06N3/088
摘要： Die Erfindung betrifft ein Verfahren und System zur Diagnose von Meldungen (M1, M2, …), die während eines Betriebs einer technischen Anlage (1) generiert werden, wobei der innerhalb der Anlage (1) ablaufende verfahrenstechnische Prozess (2) mittels eines Prozessleitsystems (3) gesteuert wird, und den Betrieb der Anlage kennzeichnende Datensätze mit Werten von Prozessvariablen (L, T, F, …) erfasst und in einem Datenspeicher (15) abgelegt werden. Anhand der Datensätze und zumindest einer vorbestimmten, so genannten selbstorganisierenden Karte werden Diagnoseaussagen über den Betrieb der Anlage (1) bestimmt. An Knoten der selbstorganisierenden Karte sind den Datensätzen entsprechende n-Tupel mit vorbestimmten Werten von Prozessvariablen (L, T, F, …) für einen fehlerfreien Betrieb der Anlage abgelegt. Die Erfindung ist dadurch gekennzeichnet, dass nach Auftreten einer Meldung (M1) für aktuelle und/oder historische Zeitintervalle, innerhalb deren die Meldung (M1) aufgetreten ist, kleinste Abstände zwischen den Datensätzen des aktuellen Zeitintervalls (t_akt) und den Datensätzen der Knoten (K) der selbstorganisierenden Karte bestimmt werden und der oder die entsprechende(n) Knoten (Kres) ausgewählt wird/werden. Anschließend werden Symptome bestimmt, indem Differenzen aus den Datensätzen der Zeitintervalle zu dem oder den zuvor bestimmten Knoten (Kres) gebildet werden, wobei nur die Differenzen von Prozessvariablen berücksichtigt werden, die einen vorgegebenen Schwellwert (S_Sym) überschreiten. Die Prozessvariablen (L, T, F, …), die an den zuvor bestimmten Symptomen beteiligt sind, werden ausgegeben. Auf diese Weise wird ein Anlagenfahrer bei der Diagnose einer Prozessmeldung unterstützt.

8. 发明申请

WO2023278102A1 SERVICE LOCATION ANOMALIES 审中-公开
公开(公告)号：WO2023278102A1
公开(公告)日：2023-01-05
申请号：PCT/US2022/032344
申请日：2022-06-06
申请人： LANDIS+GYR INNOVATIONS, INC.
发明人： GARZA, Carlos Iga
IPC分类号： H02J3/00 , H02J13/00 , G05B23/024 , G06N20/00 , H02J13/00002 , H02J2203/20 , H02J3/0012
摘要： Disclosed techniques include using machine learning to detect an electrical anomaly in a power distribution system. In an example, a method includes accessing voltage measurements measured at an electric metering device and over a time period. The method further includes calculating, from voltage measurements and for each time window of a set of time windows, a corresponding average voltage and a corresponding minimum voltage. The method further includes applying a machine learning model to the average voltages and the minimum voltages. The machine learning model is trained to identify one or more predetermined electrical anomalies from voltages. The method further includes receiving, from the machine learning model, a classification indicating an identified anomaly. The method further includes based on the classification, sending an alert to a utility operator.

9. 发明申请

WO2022009010A1 MODEL FIDELITY MONITORING AND REGENERATION FOR MANUFACTURING PROCESS DECISION SUPPORT 审中-公开
公开(公告)号：WO2022009010A1
公开(公告)日：2022-01-13
申请号：PCT/IB2021/055601
申请日：2021-06-24
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION , IBM (CHINA) INVESTMENT COMPANY LTD. , IBM DEUTSCHLAND GMBH
发明人： ZHOU, Nianjun , SUBRAMANIAN, Dharmashankar , PATEL, Dhavalkumar , BHAMIDIPATY, Anuradha
IPC分类号： G06N5/00 , G06F16/00 , G05B13/0265 , G05B13/04 , G05B23/024
摘要： Techniques for model fidelity monitoring and regeneration for manufacturing process decision support are described herein. Aspects of the invention include determining that an output of a regression model corresponding to a current time period of decision support for a manufacturing process is not within a predefined range of a historical process dataset, wherein the regression model was constructed based on the historical process dataset, and performing an accuracy and fidelity analysis on the regression model based on process data from the manufacturing process corresponding to a previous time period. Based on a result of the accuracy and fidelity analysis being below a threshold, a mismatch of the regression model as compared to the manufacturing process is determined. Based on determining the mismatch, a temporary regression model corresponding to the manufacturing process is generated, and decision support for the manufacturing process is performed based on the temporary regression model.

10. 发明申请

WO2021197782A1 DATA PROCESSING FOR INDUSTRIAL MACHINE LEARNING 审中-公开
公开(公告)号：WO2021197782A1
公开(公告)日：2021-10-07
申请号：PCT/EP2021/056093
申请日：2021-03-10
申请人： ABB SCHWEIZ AG
发明人： KLOEPPER, Benjamin , SCHMIDT, Benedikt , AMIHAI, Ido , CHIOUA, Moncef , SCHLAKE, Jan Christoph , KOTRIWALA, Arzam , HOLLENDER, Martin , JANKA, Dennis , LENDERS, Felix , ABUKWAIK, Hadil
IPC分类号： G06N20/00 , G06N7/00 , G06F17/40 , G06F16/215 , G05B23/00 , G05B23/024 , G05B23/0283 , G06N7/005
摘要： The invention relates to a computer-implemented method (100) for automating the development of industrial machine learning applications in particular for predictive maintenance, process monitoring, event prediction, or root-cause analysis. The method consists of one or more sub-methods that, depending on the industrial machine learning problem, may be executed iteratively. These sub-methods include at least one of a method to automate the data cleaning in training (S10) and later application (S15) of machine learning models, a method to label (S11) time series (in particular signal data) with help of other timestamp records, feature engineering (S12) with the help of process mining, and automated hyperparameter tuning (S14) for data segmentation and classification.

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