Computer architecture and method for modifying intake data rate based on a predictive model
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G06F-011/00
G06F-011/07
G06N-007/00
출원번호
US-0963212
(2015-12-08)
등록번호
US-10025653
(2018-07-17)
발명자
/ 주소
Goldstein, Michael
Ravensberg, Tom
Hansmann, Will
출원인 / 주소
Uptake Technologies, Inc.
대리인 / 주소
Lee Sullivan & Smith, LLP
인용정보
피인용 횟수 :
0인용 특허 :
99
초록▼
Disclosed herein is a computer architecture and software that is configured to modify data intake operation at an asset-monitoring system based on a predictive model. In accordance with the present disclosure, the asset-monitoring system may execute a predictive model that outputs an indicator of wh
Disclosed herein is a computer architecture and software that is configured to modify data intake operation at an asset-monitoring system based on a predictive model. In accordance with the present disclosure, the asset-monitoring system may execute a predictive model that outputs an indicator of whether at least one event from a group of events (e.g., a failure event) is likely to occur at a given asset within a given period of time in the future. Based on the output of this predictive model, the asset-monitoring system may modify one or more operating parameters for ingesting data from the given asset, such as a storage location for the ingested data, a set of data variables from the asset that are ingested, and/or a rate at which data from the asset is ingested.
대표청구항▼
1. A computing system comprising: a network interface configured to receive data from a plurality of assets;a data intake system configured to ingest data received from the plurality of assets;at least one processor;a non-transitory computer-readable medium; andprogram instructions stored on the non
1. A computing system comprising: a network interface configured to receive data from a plurality of assets;a data intake system configured to ingest data received from the plurality of assets;at least one processor;a non-transitory computer-readable medium; andprogram instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing system to:based on historical operating data for a plurality of assets, define a predictive model that is configured to (a) receive sensor data for an asset as input, (b) for each of at least two failure types from a group of failure types related to mechanical operation, make a respective prediction of whether the failure type is likely to occur at the asset in the future, and (c) based on the respective predictions, determine and output a health metric indicating whether at least one failure type from the group of failure types related to mechanical operation is predicted to occur at the asset in the future, wherein the historical operating data comprises (i) historical abnormal-condition data for the plurality of assets that indicates past occurrences of abnormal conditions that are associated with the group of failure types and (ii) historical sensor data for the plurality of assets that indicates sensor measurements associated with the past occurrences of abnormal conditions;operate in a first mode in which the data intake system ingests operating data received from a given asset of the plurality of assets at a first ingestion rate, wherein the operating data comprises data related to the mechanical operation of the given asset that includes abnormal-condition data and sensor data;while operating in the first mode, (a) receive operating data from the given asset; and (b) ingest at least a portion of the received operating data at the first ingestion rate, wherein the ingested portion of the received operating data includes ingested sensor data for the given asset;apply the predictive model to the ingested sensor data and thereby determine a health metric for the given asset that indicates whether at least one failure type from the group of failure types related to mechanical operation is predicted to occur at the given asset in the future;compare the health metric for the given asset to a threshold condition that defines whether an asset is considered to be in a state of impending failure and thereby make a determination that the health metric satisfies the threshold condition such that the given asset is considered to be in a state of impending failure;in response to the determination, transition from operating in the first mode to operating in a second mode in which the data intake system ingests operating data from the given asset at a second ingestion rate that is higher than the first ingestion rate; andwhile operating in the second mode, (a) receive operating data from the given asset and (b) ingest at least a portion of the received operating data at the second ingestion rate, wherein the ingested operating data comprises data related to the mechanical operation of the given asset that includes abnormal-condition data and sensor data. 2. The computing system of claim 1, wherein the health metric indicating whether at least one failure type from the group of failure types related to mechanical operation is predicted to occur at the given asset in the future comprises a probability that no failure type from the group of failure types related to mechanical operation is predicted to occur at the given asset in the future, and wherein the determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or below a threshold value. 3. The computing system of claim 1, wherein the health metric indicating whether at least one failure type from the group of failure types related to mechanical operation of the given asset is predicted to occur at the given asset in the future comprises a probability that at least one f failure type from the group of failure types related to mechanical operation of an asset is predicted to occur at the given asset in the future, and wherein the determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or above a threshold value. 4. The computing system of claim 1, wherein the second ingestion rate comprises a variable rate that is determined based on the health score. 5. The computing system of claim 1, wherein the given asset comprises a transportation machine, an industrial machine, or a utility machine. 6. The computing system of claim 1, wherein the second ingestion rate comprises a variable rate that is determined based on the comparison of the health metric to the threshold condition. 7. The computing system of claim 1, wherein the second ingestion rate comprises a variable rate that is determined based on one or more characteristics of the given asset. 8. A non-transitory computer-readable medium having instructions stored thereon that are executable to cause a computing system to: based on historical operating data for a plurality of assets, define a predictive model that is configured to (a) receive sensor data for an asset as input, (b) for each of at least two failure types from a group of failure types related to mechanical operation, make a respective prediction of whether the failure type is likely to occur at the asset in the future, and (c) based on the respective predictions, determine and output a health metric indicating whether at least one failure type from the group of failure types related to mechanical operation is predicted to occur at the asset in the future, wherein the historical operating data comprises (i) historical abnormal-condition data for the plurality of assets that indicates past occurrences of abnormal conditions that are associated with the group of failure types and (ii) historical sensor data for the plurality of assets that indicates sensor measurements associated with the past occurrences of abnormal conditions;operate in a first mode in which the computing system ingests operating data received from a given asset of the plurality of assets at a first ingestion rate, wherein the operating data comprises data related to the mechanical operation of the given asset that includes abnormal-condition data and sensor data;while operating in the first mode, (a) receive operating data from the given asset, and (b) ingest at least a portion of the received operating data at the first ingestion rate, wherein the ingested portion of the received operating data includes ingested sensor data for the given asset;apply the predictive model to the ingested sensor data and thereby determine a health metric for the given asset that indicates whether at least one failure type from the group of failure types related to mechanical operation is predicted to occur at the given asset in the future;compare the health metric for the given asset to a threshold condition that defines whether an asset is considered to be in a state of impending failure and thereby make a determination that the health metric satisfies the threshold condition such that the given asset is considered to be in a state of impending failure;in response to the determination, transition from operating in the first mode to operating in a second mode in which the computing system ingests operating data from the given asset at a second ingestion rate that is higher than the first ingestion rate; andwhile operating in the second mode, (a) receive operating data from the given asset and (b) ingest at least a portion of the received operating data at the second ingestion rate, wherein the ingested operating data comprises data related to the mechanical operation of the given asset that includes abnormal-condition data and sensor data. 9. The non-transitory computer-readable medium of claim 8, wherein the health metric indicating whether at least one failure type from the group of failure types related to mechanical operation is predicted to occur at the given asset in the future comprises a probability that no failure type from the group of failure types related to mechanical operation is predicted to occur at the given asset in the future, and wherein the determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or below a threshold value. 10. The non-transitory computer-readable medium of claim 8, wherein the health metric indicating whether at least one failure type from the group of failure types related to mechanical operation of the given asset is predicted to occur at the given asset in the future comprises a probability that at least one f failure type from the group of failure types related to mechanical operation of an asset is predicted to occur at the given asset in the future, and wherein the determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or above a threshold value. 11. The non-transitory computer-readable medium of claim 8, wherein the given asset comprises a transportation machine, an industrial machine, or a utility machine. 12. The non-transitory computer-readable medium of claim 8, wherein the second ingestion rate comprises a variable rate that is determined based on the health score. 13. The non-transitory computer-readable medium of claim 8, wherein the second ingestion rate comprises a variable rate that is determined based on the comparison of the health metric to the threshold condition. 14. A computer-implemented method comprising: based on historical operating data for a plurality of assets, defining a predictive model that is configured to (a) receive sensor data for an asset as input, (b) for each of at least two failure types from a group of failure types related to mechanical operation, make a respective prediction of whether the failure type is likely to occur at the asset in the future, and (c) based on the respective predictions, determine and output a health metric indicating whether at least one failure type from the group of failure types related to mechanical operation is predicted to occur at the asset in the future, wherein the historical operating data comprises (i) historical abnormal-condition data for the plurality of assets that indicates past occurrences of abnormal conditions that are associated with the group of failure types and (ii) historical sensor data for the plurality of assets that indicates sensor measurements associated with the past occurrences of abnormal conditions;operating a computing system in a first mode in which the computing system ingests operating data received from a given asset of the plurality of assets at a first ingestion rate, wherein the operating data comprises data related to the mechanical operation of the given asset that includes abnormal-condition data and sensor data;while operating the computing system in the first mode, (a) receiving operating data from the given asset, and (b) ingesting at least a portion of the received operating data at the first ingestion rate, wherein the ingested portion of the received operating data includes ingested sensor data for the given asset;applying the predictive model to the ingested sensor data and thereby determining a health metric for the given asset that indicates whether at least one failure type from the group of failure types related to mechanical operation is predicted to occur at the given asset in the future;comparing the health metric for the given asset to a threshold condition that defines whether an asset is considered to be in a state of impending failure and thereby making a determination that the indicator satisfies the threshold condition such that the given asset is considered to be in a state of impending failure;in response to the determination, transitioning the computing system from operating in the first mode to operating in a second mode in which the computing system ingests operating data from the given asset at a second ingestion rate that is higher than the first ingestion rate; andwhile operating the computing system in the second mode, (a) receiving operating data from the given asset and (b) ingesting at least a portion of the received operating data at the second ingestion rate, wherein the ingested operating data comprises data related to the mechanical operation of the given asset that includes abnormal-condition data and sensor data. 15. The computer-implemented method of claim 14, wherein the health metric indicating whether at least one failure type from the group of failure types related to mechanical operation is predicted to occur at the given asset in the future comprises a probability that no failure type from the group of failure types related to mechanical operation is predicted to occur at the given asset in the future, and wherein the determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or below a threshold value. 16. The computer-implemented method of claim 14, wherein the health metric indicating whether at least one failure type from the group of failure types related to mechanical operation of the given asset is predicted to occur at the given asset in the future comprises a probability that at least one f failure type from the group of failure types related to mechanical operation of an asset is predicted to occur at the given asset in the future, and wherein the determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or above a threshold value. 17. The computer-implemented method of claim 14, wherein the given asset comprises a transportation machine, an industrial machine, or a utility machine. 18. The computer-implemented method of claim 14, wherein the second ingestion rate comprises a variable rate that is determined based on the health score. 19. The computer-implemented method of claim 14, wherein the second ingestion rate comprises a variable rate that is determined based on the comparison of the health metric to the threshold condition. 20. The computer-implemented method of claim 14, wherein the second ingestion rate comprises a variable rate that is determined based on one or more characteristics of the given asset.
연구과제 타임라인
LOADING...
LOADING...
LOADING...
LOADING...
LOADING...
이 특허에 인용된 특허 (99)
Bickford, Randall L.; Palnitkar, Rahul M.; Lee, Vo, Adaptive model training system and method.
Eberbach, Andrew M.; Jemiolo, Daniel E.; Miller, Steven M.; Subramanian, Balan, Adjusting sliding window parameters in intelligent event archiving and failure analysis.
Jammu, Vinay Bhaskar; Schneider, William Roy; Bliley, Richard Gerald; Varma, Anil; Roddy, Nicholas Edward, Analyzing fault logs and continuous data for diagnostics for a locomotive.
Wegerich, Stephan W.; Wolosewicz, Andre; Xu, Xiao; Herzog, James P.; Pipke, Robert Matthew, Automated model configuration and deployment system for equipment health monitoring.
Meek, Christopher A.; Heckerman, David E.; Rounthwaite, Robert L.; Chickering, David Maxwell; Thiesson, Bo, Bayesian approach for learning regression decision graph models and regression models for time series analysis.
Salman, Mutasim A.; Zhang, Yilu; Howell, Mark N.; Tang, Xidong; Ghoneim, Youseff A.; Dorfstatter, Walter A., Co-operative on-board and off-board component and system diagnosis and prognosis.
Vipin Kewal Ramani ; Rasiklal Punjalal Shah ; Ramesh Ramachandran ; Piero Patrone Bonissone ; Yu-To Chen ; Phillip Edward Steen ; John Andrew Johnson, Diagnostic system with learning capabilities.
Sadowski, Randall P.; Campbell, Jr., John T.; Glavach, Mark A.; Miller, Scott A.; Overstreet, Keith A.; Sturrock, David T., Dynamic determination of sampling rates.
Bankert Raymond J. (Clifton Park NY) Imam Imdad (Schenectady NY) Rajiyah Harindra (Clifton Park NY), Integrated model-based reasoning/expert system diagnosis for rotating machinery.
Wang Hsu-Pin (Tallahassee FL) Huang Hsin-Hao (Kaohsiung TWX) Knapp Gerald M. (Baton Rouge LA) Lin Chang-Ching (Tallahassee FL) Lin Shui-Shun (Tallahassee FL) Spoerre Julie K. (Tallahassee FL), Machine fault diagnostics system and method.
David Richard Gibson ; Nicholas Edward Roddy ; Anil Varma, Method and system for analyzing fault and snapshot operational parameter data for diagnostics of machine malfunctions.
Yanosik, Jr., Edward Michael; Clifford, William Larson; Horejs, Timothy James; Yanosik, Ann Marie, Method and system for assessing adjustment factors in testing or monitoring process.
Subbu, Rajesh V.; Bonissone, Piero P.; Eklund, Neil H.; Iyer, Naresh S.; Shah, Rasiklal P.; Yan, Weizhong; Knodle, Chad E.; Schmid, James J., Method and system for performing model-based multi-objective asset optimization and decision-making.
Fera, Gregory J.; McQuown, Christopher M.; Reichenbach, Bryan S.; Wisniewski, Edward P., Method and system for sorting incident log data from a plurality of machines.
Bliley, Richard G.; Roddy, Nicholas E., Process and system for analyzing fault log data from a machine so as to identify faults predictive of machine failures.
Bonissone, Piero Patrone; Hershey, John Erik; Mitchell, Jr., Robert James; Subbu, Jr., Rajesh Venkat; Taware, Avinash Vinayak; Hu, Xiao, System and method for advanced condition monitoring of an asset system.
Subbu, Rajesh Venkat; Hershey, John Erik; Hu, Xiao; Mitchell, Jr., Robert James; Taware, Avinash Vinayak; Bonissone, Piero Patrone, System and method for advanced condition monitoring of an asset system.
Bonissone, Piero Patrone; Xue, Feng; Varma, Anil; Goebel, Kai Frank; Yan, Weizhong; Eklund, Neil Holger White, System and method for equipment remaining life estimation.
Goebel, Kai Frank; Bonissone, Piero Patrone; Yan, Weizhong; Eklund, Neil Holger White; Xue, Feng, System and method for equipment remaining life estimation.
Rasiklal Punjalal Shah ; Vipin Kewal Ramani ; Susan Teeter Wallenslager ; Christopher James Dailey, System and method for integrating a plurality of diagnostic related information.
Wegerich, Stephan W.; Wilks, Alan D.; Wolosewicz, Andre, System for extraction of representative data for training of adaptive process monitoring equipment.
Kasztenny, Bogdan Z.; Sollecito, Lawrence A.; Mazereeuw, Jeffrey G.; Mao, Zhihong, Systems and methods for predicting maintenance of intelligent electronic devices.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.