Patents by Inventor Nianjun Zhou

Nianjun Zhou has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240103959
    Abstract: In example aspects of this disclosure, a method includes generating, by one or more computing devices, a parametric model that expresses condition states for each of a plurality of assets, and the probability of the assets transitioning between the condition states; generating, by the one or more computing devices, stochastic degradation predictions of a group of the assets, based on the condition states and the probability of transitioning between the condition states for at least some of the assets; and generating, by the one or more computing devices, a maintenance schedule based on: the stochastic degradation predictions of the group of the assets, costs of corrective maintenance for assets in a failed state, and costs of scheduled maintenance for the assets.
    Type: Application
    Filed: September 21, 2022
    Publication date: March 28, 2024
    Inventors: Pavankumar Murali, Dzung Tien Phan, Nianjun Zhou, Lam Minh Nguyen
  • Patent number: 11868932
    Abstract: In an approach for real-time opportunity discovery for productivity enhancement of a production process, a processor extracts a set of features from time series data, through autoencoding using a neural network, based on non-control variables for the time series data. A processor identifies one or more operational modes based on the extracted features including a dimensional reduction with a representation learning from the time series data. A processor identifies a neighborhood of a current operational state based on the extracted features. A processor compares the current operational state to historical operational states based on the time series data at the same operational mode. A processor discovers an operational opportunity based on the comparison of the current operational state to the historical operational states using the neighborhood. A processor identifies control variables in the same mode which variables are relevant to the current operational state.
    Type: Grant
    Filed: September 30, 2020
    Date of Patent: January 9, 2024
    Assignee: International Business Machines Corporation
    Inventors: Nianjun Zhou, Dharmashankar Subramanian, WingHang Crystal Lui
  • Publication number: 20230376825
    Abstract: A computer-implemented method, a computer program product, and a computer system for adaptive retraining of an artificial intelligence model. A computer system computes drift magnitude scores for respective drift functions. A computer system computes an aggregated data drift score for a data drift, an aggregated concept drift score for a concept drift, and an aggregated model drift score for a model drift. A computer system computes an overall drift score, based on the aggregated data drift score, the aggregated concept drift score, the aggregated model drift score, a predetermined data drift threshold, a predetermined concept drift threshold, and a predetermined model drift threshold. A computer system determines whether retraining of the artificial intelligence model is required, based on the overall drift score. A computer system performs the retraining of the artificial intelligence model, in response to determining the retraining of the artificial intelligence model is required.
    Type: Application
    Filed: May 18, 2022
    Publication date: November 23, 2023
    Inventors: Venkata Sitaramagiridharganesh Ganapavarapu, Kyong Min Yeo, Nianjun Zhou, Wesley M. Gifford
  • Patent number: 11796991
    Abstract: Context-awareness in preventative maintenance is provided by receiving sensor data from a plurality of monitored systems; extracting a first plurality of features from a set of work orders for the monitored systems, wherein individual work orders include a root cause analysis for a context in which a nonconformance in an indicated monitored system occurred; predicting, via a machine learning model, a nonconformance likelihood for each monitored system based on the first plurality of features; selecting a subset of alerts based on predicted nonconformance likelihoods for the monitored systems; in response to receiving a user selection from the first set of alerts and a reason for the user selection, recording the reason as a modifier for the machine learning model; and updating the machine learning model to predict the subsequent nonconformance likelihoods using a second plurality of features that excludes the additional feature identified from the first plurality of features.
    Type: Grant
    Filed: March 26, 2020
    Date of Patent: October 24, 2023
    Assignee: International Business Machines Corporation
    Inventors: Nianjun Zhou, Dhaval Patel, Jayant R. Kalagnanam
  • Publication number: 20230259830
    Abstract: A computer-implemented method, computer program product, and computer system for automated model predictive control. The computer system trains multiple step look-ahead regression models, using historical states and historical actions for a to-be-optimized system, for each timestep of a past time horizon. Regression models may be either linear or nonlinear in order to capture process dynamics and nonlinearity. The computer system generates optimization constraints for each timestep of a future time horizon. The computer system generates optimization variables, based on the multiple step look-ahead regression models, for each timestep of the future time horizon. The computer system constructs a mixed integer linear programming based optimization model that includes an objective function, the optimization constraints, and the optimization variables. Nonlinear regression models are converted into piecewise linear approximation functions.
    Type: Application
    Filed: February 16, 2022
    Publication date: August 17, 2023
    Inventors: Dharmashankar Subramanian, Nianjun Zhou, Pavankumar Murali
  • Patent number: 11663679
    Abstract: A system and a method of managing a manufacturing process includes receiving production data relating to the manufacturing process and determining an operational mode associated with the manufacturing process using historical, multivariate senor data. The method may further determine a recommended action to affect production based on the determined operational mode. The operational mode may be based on at least one of: a level of operation in a continuous flow process relating to a joint set of process variables, a representation of a joint dynamic of the set of process variables over a predefined length, and a joint configuration of an uptime/downtime of a plurality of units comprising a process flow.
    Type: Grant
    Filed: October 11, 2019
    Date of Patent: May 30, 2023
    Assignee: International Business Machines Corporation
    Inventors: Nianjun Zhou, Dharmashankar Subramanian, Patrick Watson, Pavankumar Murali, Wesley M Gifford, Jayant R Kalagnanam
  • Publication number: 20230119440
    Abstract: One or more systems, computer-implemented methods and/or computer program products to facilitate a process to monitor and/or facilitate a modification to a manufacturing process. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an initialization component that identifies values of inflow data of one or more inflows of a set of inflows to a manufacturing process as control variables, and a computation optimization component that optimizes one or more intermediate flows, outflows or flow qualities of the manufacturing process using, for mode-specific regression models, decision variables that are based on a set of joint-levels of the control variables. An operation mode determination component can determine operation modes of the manufacturing process that are together defined by a set of joint-levels of the control variables.
    Type: Application
    Filed: October 19, 2021
    Publication date: April 20, 2023
    Inventors: Nianjun Zhou, Dharmashankar Subramanian
  • Patent number: 11593680
    Abstract: A computer-implemented method for providing interpretable predictions from a machine learning model includes receiving a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y). An interpretability model is built corresponding to the predictive models, by assigning an interpretability to each prediction Yi based on the hierarchical structure. Assigning the interpretability includes decomposing X into a plurality of partitions Xj using the hierarchical structure, wherein X=U1NXj, N being the number of partitions. Further, each partition is decomposed into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained. A score is computed for each partition as a function of the predicted scores of the sub-partitions, wherein the predicted scores represent interactions between the sub-partitions. Further, an interpretation of a prediction is outputted.
    Type: Grant
    Filed: July 14, 2020
    Date of Patent: February 28, 2023
    Assignee: International Business Machines Corporation
    Inventors: Nianjun Zhou, Wesley M. Gifford, Ta-Hsin Li, Pietro Mazzoleni, Pavankumar Murali
  • Publication number: 20230048378
    Abstract: Methods and systems to provide a form of probabilistic labeling to associate an outage with a disturbance, which could itself be either known based on the available data or unknown. In the latter case, labeling is especially challenging, as it necessitates the discovery of the disturbance. One approach incorporates a statistical change-point analysis to time-series events that correspond to service tickets in the relevant geographic sub-regions. The method is calibrated to separate the regular periods from the environmental disturbance periods, under the assumption that disturbances significantly increase the rate of loss-causing events. To obtain the probability that a given loss-causing event is related to an environmental disturbance, the method leverages the difference between the rate of events expected in the absence of any disturbances (baseline) and the rate of actually observed events. In the analysis, the local disturbances are identified and estimators of their duration and magnitude are provided.
    Type: Application
    Filed: August 12, 2021
    Publication date: February 16, 2023
    Inventors: Emmanuel Yashchin, Nianjun Zhou, Anuradha Bhamidipaty, Dhavalkumar C. Patel, Arun Kwangil Iyengar, Shrey Shrivastava
  • Publication number: 20220335308
    Abstract: A system and method for determining parameters for a system. Selectable questions with associated goal are presented. Operational goals are different from configurable parameters of the system. A question is selected and a goal indication is received. First values for each goal are determined by an optimization engine adjusting parameters of mathematical models for the system to improve a value of the goal associated with the selected question in a direction of the goal indication. Selectable questions and the first values are presented and selection of a second question and a second goal indication is received. An optimization engine determines second updated values by adjusting parameters of the mathematical model to improve a value of a goal associated with the second selected question in the direction of the second goal indication. Second updated values of the goals, and differences from the first updated values. are presented.
    Type: Application
    Filed: April 15, 2021
    Publication date: October 20, 2022
    Inventors: Nianjun ZHOU, Viktoriia KUSHERBAEVA, Dharmashankar SUBRAMANIAN, Xiang MA, Jacqueline WILLIAMS, Nathaniel MILLS
  • Patent number: 11422545
    Abstract: 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 having forward dependence to a target variable, a second time series of downstream sensor data having backward dependence to the target variable and a time series of measured target variable data associated with the target variable. The target variable has measuring frequency which is lower than the measuring frequencies associated with the upstream sensor data and the downstream sensor data. The hybrid sensor can estimate a value of the target variable at a given time, for example, during which no actual measured target variable value is available.
    Type: Grant
    Filed: June 8, 2020
    Date of Patent: August 23, 2022
    Assignee: International Business Machines Corporation
    Inventors: Nianjun Zhou, Dharmashankar Subramanian, Wesley M Gifford
  • Publication number: 20220188775
    Abstract: A computer implemented federated learning method of predicting failure of assets includes generating a local model at a local site for each of the cohorts and training the local model on local data for each of the cohorts for each failure type. The local model is shared with a central database. A global model is created based on an aggregation of a plurality of the local models from a plurality of the local sites. At each of the plurality of local sites, one of the global model and the local model is chosen for each of the cohorts. The chosen model operates on local data to predict failure of the assets. The utilized features include partitioning features of the assets into static features, semi-static features, and dynamic features, and forming cohorts of the assets based on the static features and the semi-static features.
    Type: Application
    Filed: December 15, 2020
    Publication date: June 16, 2022
    Inventors: Nianjun Zhou, Dhavalkumar C. Patel, Anuradha Bhamidipaty
  • Patent number: 11295197
    Abstract: The disclosure relates to extraction of rationales for studied outcome. A method comprises: grouping features as expert to align with a set of operating practices; generating interpretable features using operating rules, combining with statistical dependence analysis to bin selected features to generate favorite practice actions; grouping features as expert that combine a subset of the interpretable features to align with a set of operating practices.
    Type: Grant
    Filed: August 27, 2018
    Date of Patent: April 5, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Pavankumar Murali, Nianjun Zhou, Ta-Hsin Li, Pietro Mazzoleni, Wesley Gifford
  • Publication number: 20220101225
    Abstract: In an approach for real-time opportunity discovery for productivity enhancement of a production process, a processor extracts a set of features from time series data, through autoencoding using a neural network, based on non-control variables for the time series data. A processor identifies one or more operational modes based on the extracted features including a dimensional reduction with a representation learning from the time series data. A processor identifies a neighborhood of a current operational state based on the extracted features. A processor compares the current operational state to historical operational states based on the time series data at the same operational mode. A processor discovers an operational opportunity based on the comparison of the current operational state to the historical operational states using the neighborhood. A processor identifies control variables in the same mode which variables are relevant to the current operational state.
    Type: Application
    Filed: September 30, 2020
    Publication date: March 31, 2022
    Inventors: Nianjun Zhou, Dharmashankar Subramanian, WingHang Crystal Lui
  • Patent number: 11263103
    Abstract: Embodiments of the invention are directed a computer-implemented method for efficiently assessing data quality metrics. A non-limiting example of the computer-implemented method includes receiving, using a processor, a plurality of updates to data points in a data stream. The processor is further used to provide a plurality of data quality metrics (DQMs), and to maintain information on how much the plurality of DQMs are changing over time. The processor also maintains information on computational overhead for the plurality of DQMs, and also updates data quality information based on the maintained information.
    Type: Grant
    Filed: July 31, 2020
    Date of Patent: March 1, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Arun Kwangil Iyengar, Anuradha Bhamidipaty, Dhavalkumar C. Patel, Shrey Shrivastava, Nianjun Zhou
  • Publication number: 20220035721
    Abstract: Embodiments of the invention are directed a computer-implemented method for efficiently assessing data quality metrics. A non-limiting example of the computer-implemented method includes receiving, using a processor, a plurality of updates to data points in a data stream. The processor is further used to provide a plurality of data quality metrics (DQMs), and to maintain information on how much the plurality of DQMs are changing over time. The processor also maintains information on computational overhead for the plurality of DQMs, and also updates data quality information based on the maintained information.
    Type: Application
    Filed: July 31, 2020
    Publication date: February 3, 2022
    Inventors: Arun Kwangil Iyengar, Anuradha Bhamidipaty, Dhavalkumar C. Patel, Shrey Shrivastava, Nianjun Zhou
  • Publication number: 20220027685
    Abstract: A computer implemented method for automatically generating an optimization model for site-wide plant optimization includes mapping a process flow diagram of a plant process to a graph comprising nodes and edges, wherein the nodes represent processes and the edges represent flows between processes. A behavior is learned for each node of the graph based at least on historic data of the plant process. One or more regression functions are modeled for each node to predict an output of each of the processes, wherein the one or more regression functions are modeled based on the learned behavior for each node.
    Type: Application
    Filed: July 24, 2020
    Publication date: January 27, 2022
    Inventors: Dzung Tien Phan, Lam Nguyen, Pavankumar Murali, Nianjun Zhou
  • Publication number: 20220019911
    Abstract: A computer-implemented method for providing interpretable predictions from a machine learning model includes receiving a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y). An interpretability model is built corresponding to the predictive models, by assigning an interpretability to each prediction Yi based on the hierarchical structure. Assigning the interpretability includes decomposing X into a plurality of partitions Xj using the hierarchical structure, wherein X=U1NXj, N being the number of partitions. Further, each partition is decomposed into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained. A score is computed for each partition as a function of the predicted scores of the sub-partitions, wherein the predicted scores represent interactions between the sub-partitions. Further, an interpretation of a prediction is outputted.
    Type: Application
    Filed: July 14, 2020
    Publication date: January 20, 2022
    Inventors: NIANJUN ZHOU, WESLEY M. GIFFORD, TA-HSIN LI, PIETRO MAZZOLENI, PAVANKUMAR MURALI
  • Publication number: 20220011760
    Abstract: 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.
    Type: Application
    Filed: July 8, 2020
    Publication date: January 13, 2022
    Inventors: Nianjun Zhou, Dharmashankar Subramanian, Dhavalkumar C. Patel, Anuradha Bhamidipaty
  • Patent number: 11204851
    Abstract: Embodiments of the invention are directed a computer-implemented method for assessing data quality. A non-limiting example of the computer-implemented method includes using a processor to receive a plurality of updates to data points in a data stream. The processor is further used to compute instances of a data quality metric (DQM) from the data points in the data stream. The instances of the DQM are configured to differentiate the data points in the data stream by time and assign a higher weight to the instances of the DQM computed from more recent data points in the data stream. The instances of the DQM are continuously updated as more of the data points are received by the processor while limiting cycles of the processor consumed by updating the instances of the DQM to a threshold.
    Type: Grant
    Filed: July 31, 2020
    Date of Patent: December 21, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Arun Kwangil Iyengar, Anuradha Bhamidipaty, Dhavalkumar C. Patel, Shrey Shrivastava, Nianjun Zhou