-
Patent Agency Ranking
1.发明申请公开(公告)号：US20230115272A1
公开(公告)日：2023-04-13
申请号：US17860838
申请日：2022-07-08
Applicant: Tula Technology, Inc.
Inventor： Louis J. SERRANO , Elliott A. ORTIZ-SOTO , Shikui Kevin CHEN , Li-Chun CHIEN , Aditya MANDAL
Abstract: A classifier capable of predicting if cylinder valves of an engine commanded to activate or deactivate failed to activate or deactivate respectively. In various embodiments, the classifier can be binary or multi-class Logistic Regression, or a Multi-Layer Perceptron (MLP) classifier. The variable displacement engine can operate in cooperation with a variable displacement engine using cylinder deactivation (CDA) or skip fire, including dynamic skip fire and/or multi-level skip fire.
-
公开(公告)号：US20210003088A1
公开(公告)日：2021-01-07
申请号：US17026706
申请日：2020-09-21
Applicant: Tula Technology, Inc.
Inventor： Shikui Kevin CHEN , Aditya MANDAL , Li-Chun CHIEN , Elliott ORTIZ-SOTO
Abstract: Using machine learning for cylinder misfire detection in a dynamic firing level modulation controlled internal combustion engine is described. In a classification embodiment, cylinder misfires are differentiated from intentional skips based on a measured exhaust manifold pressure. In a regressive model embodiment, the measured exhaust manifold pressure is compared to a predicted exhaust manifold pressure generated by neural network in response to one or more inputs indicative of the operation of the vehicle. Based on the comparison, a prediction is made if a misfire has occurred or not. In yet other alternative embodiment, angular crank acceleration is used as well for misfire detection.
-
公开(公告)号：US20220205398A1
公开(公告)日：2022-06-30
申请号：US17137955
申请日：2020-12-30
Applicant: Tula Technology, Inc.
Inventor： Shikui Kevin CHEN , Aditya MANDAL , Louis J. SERRANO , Xiaoping CAI
Abstract: A system and method for the use of machine learning for detecting faults for cylinder intake and/or exhaust valves that do not properly open or close as commanded and for generating a flag for such faults.
-
公开(公告)号：US20220010744A1
公开(公告)日：2022-01-13
申请号：US17407984
申请日：2021-08-20
Applicant: Tula Technology, Inc.
Inventor： Shikui Kevin CHEN , Aditya MANDAL , Li-Chun CHIEN , Elliott ORTIZ-SOTO
Abstract: Using machine learning for cylinder misfire detection in a dynamic firing level modulation controlled internal combustion engine is described. In a classification embodiment, cylinder misfires are differentiated from intentional skips based on a measured exhaust manifold pressure. In a regressive model embodiment, the measured exhaust manifold pressure is compared to a predicted exhaust manifold pressure generated by neural network in response to one or more inputs indicative of the operation of the vehicle. Based on the comparison, a prediction is made if a misfire has occurred or not. In yet other alternative embodiment, angular crank acceleration is used as well for misfire detection.
-
5.发明申请公开(公告)号：US20190145859A1
公开(公告)日：2019-05-16
申请号：US16180703
申请日：2018-11-05
Applicant: Tula Technology, Inc.
Inventor： Shikui Kevin CHEN , Aditya MANDAL , Li-Chun CHIEN , Elliot ORTIZ-SOTO
Abstract: Using machine learning for misfire detection in a Dynamic firing level modulation controlled internal combustion engine is described. A neural network is used to calculate expected crank acceleration from various inputs, including the dynamically defined cylinder skip fire sequence. The output of the neural network is then compared to a signal indicative of the measured crank acceleration. Based the comparison, a prediction is made if a misfire has occurred or not. In alternative embodiment, the neural network is expanded to include the measured crank acceleration as an additional input. With the latter embodiment, the neural network is arranged to directly predict misfire events.
-
-
-
-
Search Results
5
records
(took 0.012 seconds)
