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1. 发明申请

WO2022136108A1 AUTOMATIC IDENTIFICATION OF IMPORTANT BATCH EVENTS 审中-公开
公开(公告)号：WO2022136108A1
公开(公告)日：2022-06-30
申请号：PCT/EP2021/086189
申请日：2021-12-16
申请人： ABB SCHWEIZ AG
发明人： HOLLENDER, Martin , SCHMIDT, Benedikt
IPC分类号： G05B13/04 , G05B19/418 , G06F9/48
摘要： Method for automatic identification of important batch events for a batch execution alignment algorithm, comprising: receiving (S10) historical batch data of a batch process, wherein the historical batch data comprises a plurality of batch executions (10, 20), and wherein each of the plurality of batch executions (10, 20) comprises a plurality of batch events (E11, E12, E21, E22), indicating a specific event of the batch process, and at least one time series of a process variable (V1, V2, V3), indicating a development of the process variable during the batch process; determining (S20) a distance between the at least one time series (V1, V 2, V3) of the plurality of the batch executions (10, 20) for each of the plurality of batch events (E11, E12, E21, E22); and identifying (S30) at least one important batch event (Ml) for a batch execution alignment algorithm with a smallest distance using the determined distances.

2. 发明申请

WO2021198357A1 METHOD OF TRANSFER LEARNING FOR A SPECIFIC PRODUCTION PROCESS OF AN INDUSTRIAL PLANT 审中-公开
公开(公告)号：WO2021198357A1
公开(公告)日：2021-10-07
申请号：PCT/EP2021/058477
申请日：2021-03-31
申请人： ABB SCHWEIZ AG
发明人： SCHMIDT, Benedikt , AMIHAI, Ido , KOTRIWALA, Arzam , CHIOUA, Moncef , JANKA, Dennis , LENDERS, Felix , SCHLAKE, Jan Christoph , HOLLENDER, Martin , ABUKWAIK, Hadil , KLOEPPER, Benjamin
IPC分类号： G06N3/08 , G06N20/00 , G05B13/0265 , G05B17/02 , G05B19/41885 , G05B2219/32352 , G06N3/0454 , G06N3/082 , G06N3/088 , G06N5/022
摘要： The invention relates to a method of transfer learning for a specific production process of an industrial plant, comprises the steps: providing (S10) a plurality of data templates (T) defining expected data for a production process; providing (S20) plant data (P) of the industrial plant, comprising data points of the specific production process, wherein the data points (P) comprise information about input and output of the specific production process; wherein the data template (T) defines a grouping for the expected data according to their relation in the industrial plant; determining (S30) a process instance of the specific production process, defining a mapping between the plant data (P) to the expected data of the specific production process; determining (S40) historic process data, being historic sensor data relating to the specific production process using the determined process instance; determining (S50) training data using the determined process instance (I) and the determined historic process data (H); wherein the training data comprises a structured data matrix, wherein columns of the data matrix represent the sensor data that are grouped in accordance with the data template (T) and wherein rows of the data matrix represent timestamps of obtaining the sensor data; providing (S60) a pre-trained machine learning model using the determined process instance; and training (S70) a new machine learning model using the provided pre-trained model and the determined training data.

3. 发明申请

WO2023088665A1 TRAINING PREDICTION MODELS FOR PREDICTING UNDESIRED EVENTS DURING EXECUTION OF A PROCESS 审中-公开
公开(公告)号：WO2023088665A1
公开(公告)日：2023-05-25
申请号：PCT/EP2022/080267
申请日：2022-10-28
申请人： ABB SCHWEIZ AG
发明人： ABUKWAIK, Hadil , SHARMA, Divyasheel , KLOEPPER, Benjamin , KOTRIWALA, Arzam Muzaffar , RODRIGUEZ, Pablo , SCHMIDT, Benedikt , TAN, Ruomu , K R, Chandrika , BORRISON, Reuben , DIX, Marcel , DOPPELHAMER, Jens
IPC分类号： G05B23/02 , G06N3/08
摘要： A method (100) for training a prediction model (1) for predicting the likelihood that at least one predetermined undesired event will occur during execution of a process (2) using training samples (3), wherein each training sample (3) comprises data that characterizes a state of the process (2), and the method (100) comprises the steps of: obtaining (110) training samples (3) representing states of the process (2) that do not cause the undesired event, and labelling these training samples with a pre-set low likelihood of the undesired event occurring; obtaining (120), based at least in part on a process model (2a) and a set of predetermined rules (2b) that stipulate in which states of the process (2) there is an increased likelihood of the undesired event occurring, further training samples (4) representing states of the process (2) with an increased likelihood to cause the undesired event, and labelling these training samples (4) with said increased likelihood; providing (130) training samples (3, 4) to the to-be-trained prediction model (1), so as to obtain, from the prediction model (1), a prediction (5) of the likelihood for occurrence of the undesired event in a state of the process (2) represented by the respective sample (3, 4); rating (140) a difference between the prediction (5) and the label of the respective sample (3, 4) by means of a predetermined loss function (6); and optimizing (150) parameters (1a) that characterize the behavior of the prediction model (1), such that, when predictions (5) on further samples (3, 4) are made, the rating (6a) by the loss function (6) is likely to improve.

4. 发明申请

WO2022171371A1 GENERATING EXECUTION PROTOCOLS FOR PERFORMING STANDARD OPERATING PROCEDURES IN INDUSTRIAL PLANTS 审中-公开
公开(公告)号：WO2022171371A1
公开(公告)日：2022-08-18
申请号：PCT/EP2022/050400
申请日：2022-01-11
申请人： ABB SCHWEIZ AG
发明人： SCHMIDT, Benedikt , DOPPELHAMER, Jens , RODRIGUEZ, Pablo , KLOEPPER, Benjamin , BORRISON, Reuben , DIX, Marcel , ABUKWAIK, Hadil , KOTRIWALA, Arzam Muzaffar , MACZEY, Sylvia , ZIOBRO, Dawid , LINGE, Simon , GAERTLER, Marco , SHARMA, Divyasheel , K R, Chandrika , GOPALAKRISHNAN, Gayathri , BERNING, Matthias
IPC分类号： G05B19/418 , G06Q10/06
摘要： A computer-implemented method (100) for generating and/or augmenting an execution protocol (4) for at least one standard operating procedure, SOP (2), in an industrial plant (1), comprising the steps of: • providing (110) at least one SOP (2) of the plant (1), said SOP (2) comprising a plurality of steps (2a-2g); • providing (120) measurement data (3) that has been acquired during multiple executions of the at least one SOP (2) and is indicative of actions performed in the plant (1) that modify the state and/or the behavior of the plant (1) or any part thereof for the purpose of executing the SOP (2); • for each step (2a-2g) of the SOP (2), determining (130), from the measurement data (3), a subset (3a-3g) of the measurement data (3) that is indicative of actions performed for the purpose of executing this particular step (2a-2g) of the SOP; and • aggregating (140) the subset (3a-3g) of the measurement data (3) determined for each step (2a-2g) of the SOP (2) into at least one instruction (4a-4g) for executing this particular step (2a-2g) of the SOP, wherein this instruction (4a-4g) is part of the sought protocol (4).

5. 发明申请

WO2022161699A1 METHOD FOR ANALYSING PROCESS DATA RELATED TO A SEGMENT OF A PRODUCTION PROCESS 审中-公开
公开(公告)号：WO2022161699A1
公开(公告)日：2022-08-04
申请号：PCT/EP2021/086161
申请日：2021-12-16
申请人： ABB SCHWEIZ AG
发明人： HOLLENDER, Martin , SCHMIDT, Benedikt , TAN, Ruomu , XU, Chaojun , VOLKMANN, Lara-Marie
IPC分类号： G06F16/2455 , G06F16/2457
摘要： A method for analysing process data related to a segment of a production process as described including the following steps: providing a process data sequence of the segment of the production process exhibiting a data pattern of at least one process variable to be analysed; providing a set of metadata of the segment of the production process related to the provided process data sequence; determining process data sequences based on the provided set of metadata of the segment of the production process, which are stored in a first database; determining a start timestamp and end timestamp of each of the determined process data sequence, based on the first database; and calculating a similarity value for each of the determined process data sequences compared to the provided process data sequence, based on the data pattern of the at least one process variable, wherein the determined process data sequences for the calculation are provided, based on the related start timestamps and end timestamps, by accessing a second database comprising the process data sequences, for analysing the process data.

6. 发明申请

WO2021219429A1 METHOD FOR GENERATING A PROCESS MODEL 审中-公开
公开(公告)号：WO2021219429A1
公开(公告)日：2021-11-04
申请号：PCT/EP2021/060186
申请日：2021-04-20
申请人： ABB SCHWEIZ AG
发明人： SCHMIDT, Benedikt , DIX, Marcel , HOLLENDER, Martin , COHEN, Andrew , KOTRIWALA, Arzam , GAERTLER, Marco , MACZEY, Sylvia , KLOEPPER, Benjamin
IPC分类号： G05B19/042 , G05B17/02
摘要： A method (100) for generating a process model modeling a manual mode procedure instance of a plant process is proposed, the procedure instances comprising a related sequence of operational actions, i.e. mining of historical plant logs to generate a workflow model, including the steps of: Providing a plurality of log events (S1) of a plurality of operational actions (110) of the plant process; Selecting a plurality of related sequences of manual mode operational actions (S2) from the plurality of log events; Filtering the plurality of related sequences of manual mode operational actions according to an individual plant section (S3); Identifying a sequential order (S4) from the plurality of filtered related sequences of the manual mode operational actions; Determining statistical properties of values of related process variables and/or statistical properties of values of related set point changes (S5) to each sequential ordered manual mode operational action from the plurality of filtered related sequences of the manual mode operational actions; Generating the process model (S6) of the manual mode procedure instance by arranging related manual mode operational actions with the sequential order of each operational action assigned with the statistical properties of the values of related process variables and/or assigned with the statistical properties of the values of the related set point changes.

7. 发明申请

WO2021197796A1 METHOD AND APPARATUS FOR MONITORING MACHINE LEARNING MODELS 审中-公开
公开(公告)号：WO2021197796A1
公开(公告)日：2021-10-07
申请号：PCT/EP2021/056180
申请日：2021-03-11
申请人： ABB SCHWEIZ AG
发明人： SCHMIDT, Benedikt , AMIHAI, Ido , CHIOUA, Moncef , KOTRIWALA, Arzam , HOLLENDER, Martin , JANKA, Dennis , LENDERS, Felix , SCHLAKE, Jan Christoph , KLOEPPER, Benjamin , ABUKWAIK, Hadil
IPC分类号： G05B23/02 , G06N3/08 , G05B13/02 , G05B17/02 , G05B13/027 , G05B23/024 , G06N20/00
摘要： The invention relates to the field of monitoring or controlling an industrial process, particularly by means of an artificial neural net. The method comprises the steps of: training the first control model (10) by means of a first set of input data (31) as first input (11), resulting in a trained first control model (10T); copying the trained first control model (10T) to a second control model (20), wherein, after copying, the second input layer (22) and the plurality of second hidden layers (24) is identical to the plurality of first hidden layers (14), and the first output layer (18) is replaced by the second output layer (28); freezing the plurality of second hidden layers (24); training the second control model (20) by means of the first set of input data (31) as second input (21), resulting in a trained second control model (20T); and running the trained second control model (20T) by means of a second set of input data (32) as second input (21), wherein the second output (29) outputs the quality measure (qm) of the first control model (10).

8. 发明申请

WO2018108429A1 COMPUTER SYSTEM AND METHOD FOR MONITORING THE STATUS OF A TECHNICAL SYSTEM 审中-公开
公开(公告)号：WO2018108429A1
公开(公告)日：2018-06-21
申请号：PCT/EP2017/079397
申请日：2017-11-16
申请人： ABB SCHWEIZ AG
发明人： KLÖPPER, Benjamin , SCHMIDT, Benedikt
IPC分类号： G05B23/02
摘要： A computer system (100), computer-implemented method and computer program product is provided for monitoring the technical status of a particular technical system (300). The computer system has an interface module (110) to receive, in a low-precision mode (LPM), status data (SD1) generated from sensor data of one or more sensors (S1 to Sn), the status data (SD1) reflecting technical parameters of the particular technical system (300) wherein the low-precision mode (LPM) is characterized by the received status data (SD1) complying with a first precision level. Further, the interface can send one or more instructions (I1) for the one or more sensors (S1 to SN), the one or more instructions (I1) configured to cause the one or more sensors to generate further status data (SD2) complying with a second precision level, the second precision level being associated with higher data accuracy than the first precision level. The further status data (SD2) complying with the second precision level is received by the interface and provided to a data analyzer (140). A Machine Learning module (120) applies a low-precision Machine Learning Model to the received status data (SD1) wherein the low-precision data model is trained on training data complying with the first precision level to analyze the received status data for one or more indicators of an abnormal technical status, and to detect an abnormality indicator. A command generator module (130) generates the one or more instructions (I1) for the one or more sensors (S1 to Sn) in case the abnormality indicator is detected.

9. 发明申请

WO2022171369A1 IMPROVING THE CONTROL STRATEGY OF DISTRIBUTED CONTROL SYSTEMS BASED ON OPERATOR ACTIONS 审中-公开
公开(公告)号：WO2022171369A1
公开(公告)日：2022-08-18
申请号：PCT/EP2022/050227
申请日：2022-01-07
申请人： ABB SCHWEIZ AG
发明人： RODRIGUEZ, Pablo , DOPPELHAMER, Jens , KLOEPPER, Benjamin , BORRISON, Reuben , DIX, Marcel , SCHMIDT, Benedikt , ABUKWAIK, Hadil , KOTRIWALA, Arzam Muzaffar , MACZEY, Sylvia , ZIOBRO, Dawid , LINGE, Simon , GAERTLER, Marco , SHARMA, Divyasheel , K R, Chandrika , GOPALAKRISHNAN, Gayathri , BERNING, Matthias , BRAUN, Roland
IPC分类号： G05B19/042 , G05B19/418 , G05B19/05 , G05B19/18 , G05B23/02
摘要： Method for amending and/or augmenting an engineering tool configured to generate application code which, when executed will cause an industrial plant to be controlled, the method comprising: - acquiring state variables that characterize an operational state of the industrial plant; - acquiring a set of interaction events of a plant operator interacting with the distributed control system via a HMI; - determining in part on the interaction events, the state variables whether one or more interaction events are indicative of the plant operator executing a task that is not sufficiently covered by the present engineering of the distributed control system; - and if this determination is positive, mapping the input data to an amendment and/or augmentation for the engineering tool that has generated the application code for the distributed control system, such that, when the application code is re-generated the plant operator is likely to manually interact with the distributed control system less frequently.

10. 发明申请

WO2022122635A1 MONITORING INDUSTRIAL EQUIPMENT WITH AT LEAST ONE ROTATING MEMBER 审中-公开
公开(公告)号：WO2022122635A1
公开(公告)日：2022-06-16
申请号：PCT/EP2021/084350
申请日：2021-12-06
申请人： ABB SCHWEIZ AG [CH]/[CH]
发明人： SCHMIDT, Benedikt , SOSALE, Guruprasad , SUBBIAH, Subanatarajan , GITZEL, Ralf , SPOORENDONK, Jonas
IPC分类号： G05B19/418 , G05B23/02 , G06N3/04
摘要： A method (100) for monitoring an equipment (1) with at least one rotating member, comprising the steps of: recording (110), by at least one sensor (2), at least one sample (3) of noise and/or vibration generated by the equipment (1) in the time domain; obtaining (120) at least one representation (3') of the at least one sample (3) in the frequency domain; obtaining (125) information (7) that is indicative at least of the type of the equipment (1); selecting (126), based on this information (7), one model (4a-4c) of the equipment (1) from a plurality of available models (4a-4c); and mapping (130), by means of the selected model (4a-4c), the at least one representation (3') to an operating state (5) of the equipment, wherein said operating state comprises a normal state, an abnormal state, a fault, and/or a degradation state (5).

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