Patents Assigned to Presenso, Ltd.
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Patent number: 11403551Abstract: A system and method for validating unsupervised machine learning models. The method includes: analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern; injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset; and analyzing the artificial dataset to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.Type: GrantFiled: June 13, 2018Date of Patent: August 2, 2022Assignee: Presenso, Ltd.Inventors: David Lavid Ben Lulu, Eitan Vesely
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Patent number: 11243524Abstract: A system and method for unsupervised root cause analysis of machine failures. The method includes analyzing, via at least unsupervised machine learning, a plurality of sensory inputs that are proximate to a machine failure, wherein the output of the unsupervised machine learning includes at least one anomaly; identifying, based on the output at least one anomaly, at least one pattern; generating, based on the at least one pattern and the proximate sensory inputs, an attribution dataset, the attribution dataset including a plurality of the proximate sensory inputs leading to the machine failure; and generating, based on the attribution dataset, at least one analytic, wherein the at least one analytic includes at least one root cause anomaly representing a root cause of the machine failure.Type: GrantFiled: July 5, 2018Date of Patent: February 8, 2022Assignee: Presenso, Ltd.Inventors: David Lavid Ben Lulu, David Almagor
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Publication number: 20200209111Abstract: A system and computer-implemented method for detecting anomalies in industrial machine sensor data, including: computing, based on a received suspected anomalous level value of a sensory input data of each of the a plurality of sensory input data of a plurality of industrial machines that are located within a predetermined proximity, an average anomalous amount that is associated with at least a time interval; and determining that at least one of the plurality of suspected anomalies is an anomaly when a result of a subtraction of the computed average anomalous amount from each suspected anomalous level value of the plurality of sensory input data exceeds a predetermined threshold.Type: ApplicationFiled: December 17, 2019Publication date: July 2, 2020Applicant: Presenso, Ltd.Inventors: David LAVID BEN LULU, Nir DROMI, Aleksandr TOLSTOV, ILIA SERGEEVICH SMYSHLIAEV
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Publication number: 20200166921Abstract: A system and computer-implemented method for identifying and repairing suboptimal operation of a machine, the computer-implemented method including: monitoring sensory input data related to an industrial machine; analyzing, using an unsupervised machine learning model, the monitored sensory inputs, wherein the output of the unsupervised machine learning model includes at least one indicator; identifying, based on the at least one indicator, at least one behavioral pattern related to the industrial machine, wherein each of the at least one behavioral pattern is indicative of at least one suboptimal operation of the industrial machine; selecting at least one corrective action based on the at least one behavioral pattern; and performing the at least one corrective action on the industrial machine.Type: ApplicationFiled: November 26, 2019Publication date: May 28, 2020Applicant: Presenso, Ltd.Inventors: David LAVID BEN LULU, Waseem GHRAYEB
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Publication number: 20180348747Abstract: A system and method for unsupervised root cause analysis of machine failures. The method includes analyzing, via at least unsupervised machine learning, a plurality of sensory inputs that are proximate to a machine failure, wherein the output of the unsupervised machine learning includes at least one anomaly; identifying, based on the output at least one anomaly, at least one pattern; generating, based on the at least one pattern and the proximate sensory inputs, an attribution dataset, the attribution dataset including a plurality of the proximate sensory inputs leading to the machine failure; and generating, based on the attribution dataset, at least one analytic, wherein the at least one analytic includes at least one root cause anomaly representing a root cause of the machine failure.Type: ApplicationFiled: July 5, 2018Publication date: December 6, 2018Applicant: Presenso, Ltd.Inventors: David LAVID BEN LULU, David ALMAGOR
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Publication number: 20180307218Abstract: A system and method for allocating machine behavioral models. The method includes analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; selecting, based on the output at least one normal behavior pattern, at least one machine behavioral model; generating, based on the selected at least one machine behavioral model, an optimal machine behavioral model representing behavior of the machine; and allocating the generated optimal machine behavioral model to the machine.Type: ApplicationFiled: June 27, 2018Publication date: October 25, 2018Applicant: Presenso, Ltd.Inventor: David LAVID BEN LULU
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Publication number: 20180293125Abstract: A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.Type: ApplicationFiled: June 12, 2018Publication date: October 11, 2018Applicant: Presenso, Ltd.Inventors: David LAVID BEN LULU, Eitan VESELY
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Publication number: 20180293516Abstract: A system and method for validating unsupervised machine learning models. The method includes: analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern; injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset; and analyzing the artificial dataset to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.Type: ApplicationFiled: June 13, 2018Publication date: October 11, 2018Applicant: Presenso, Ltd.Inventors: David LAVID BEN LULU, Eitan VESELY