Hitachi, Ltd.

Hewlett-packard Development Company, L.P.

Hewlett Packard Enterprise Development LP

Other

Chetan Kumar Gupta

Abhay Mehta

Ahmed Khairy Farahat

Song Wang

Umeshwar Dayal

Haiyan Wang

Susumu Serita

Kosta Ristovski

Qiyao Wang

Dipanjan Ghosh

Chi Zhang

Maria Teresa Gonzalez Diaz

Krishnamurthy Viswanathan

Shuai Zheng

Hamed Khorasgani

Mo Liu

Adriano Arantes

Meichun Hsu

Wei T Huang

Craig Peter Sayers

Maria G Castellanos

Ismail Ari

Elke Angelika Rundensteiner

Marcos Vieira

Mahalia Katherine Miller

Huijuan Shao

Hideki Nakamura

Ming C Hao

Harumi Kuno

Wei Huang

Choudur K Lakshminarayan

Tomoaki Hiruta

Archana Sulochana Ganapathi

Takashi Saeki

Hsiu-Khuern Tang

Riddhiman Ghosh

Yin Wang

Ravigopal Vennelakanti

Michiko Yoshida

Mahbubul Alam

Jiro Hashizume

Anshuman Sahu

Sergey Serebryakov

Tsubasa Watanabe

Koichiro Iijima

Arman Hasanzadehmoghimi

Lasitha Vidyaratne

Aman Kumar

Xian Yeow Lee

Walid Shalaby

Atsuki Kiuchi

Takuya Kanazawa

Onur Atan

Umeahwar Dayal

Lakshminarayn Choudur

Lijing Wang

Aniruddha Rajendra Rao

Pramod Singh

Karan Aggarwal

Medhabi Ray

Abdullah Al Mueen

Abbay Mehta

Phawis Thammasorn

Hideaki Suzuki

Maria Teresa Gonzales Diaz

Conventional simulation-based optimization, even when automated, requires substantial user-involved development time. Accordingly, embodiments are disclosed to automate various aspects of simulation-based optimization. In particular, an optimization configurator and associated data structures are disclosed for generating and running optimization templates that can be easily constructed (e.g., via lists of available components), revised, evaluated, and re-run as needed. The optimization templates may comprise a plurality of optimization configurations that each define and pair an optimization algorithm with a simulator of a real-world system. Embodiments can reduce development time, are applicable to various domains, can be used by novice users without specialized knowledge, and can improve the overall quality of optimization for the operations of real-world systems, such as supply chains.

Simulation-based optimization configurator to support rapid decision-making

A method for reinforcement learning (RL) of continuous actions. The method may include receiving a state as input to at least one actor network to predict candidate actions based on the state, wherein the state is a current observation; outputting the candidate actions from the at least one actor network; receiving the state and the candidate actions as inputs to a plurality of distributional critic networks, wherein the plurality of distributional critic networks calculates quantiles of a return distribution associated with the candidate actions in relation to the state; outputting the quantiles from the plurality of distributional critic networks; and selecting an output action based on the candidate actions and the quantiles.

Uncertainty-aware continuous control system based on reinforcement learning

Systems and methods for constructing an equipment troubleshooting tree involve receiving an unstructured text source and using a parsing tool to segment it into sections that are associated with equipment components, units, or processes. If the parsing tool fails, a pre-trained language model is used to identify the sections using the extracted listing as content. Metadata, including section-specific text and page ranges, are obtained for each section. A non-static process enhances similarity matching, resulting in segmented data. An indexing process embeds this data, along with the metadata, into index nodes to improve retrieval efficiency. The pre-trained language model generates a catalog of false, causes, or repair recommendations, which are structured as index notes in the troubleshooting tree. In response to a user query, the constructed troubleshooting tree is generated and output after a grounding process cross-references it with the unstructured text to ensure accuracy.

Systems and method for constructing troubleshooting trees

A method for detecting an anomaly in time series sensor data. The method may include identifying a noisiest cycle from the time series sensor data; for an evaluation of the noisiest cycle indicative of the anomaly being detected at a confidence level above a threshold, providing an output associated with the noisiest cycle as being the anomaly; and for the evaluation of the noisiest cycle indicative of the anomaly being detected at the confidence level not above the threshold: identifying a cycle from the time series sensor data having a most differing shape; and providing the output associated with the cycle having the most differing shape as being the anomaly.

Versatile anomaly detection system for industrial systems

In some examples, a computer system may receive sensor data and failure data for equipment. The system may determine, for the equipment, a plurality of time between failure (TBF) durations that are longer than other TBF durations for the equipment. The system may determine, from the sensor data corresponding to operation of the equipment during the plurality of TBF durations, a plurality of measured sensor values for the equipment. Additionally, the system may determine a subset of the measured sensor values corresponding to a largest number of the TBF durations of the plurality of TBF durations. The system may further determine at least one operating parameter value for the equipment based on the subset of the measured sensor values. The system may send a control signal for operating the equipment based on the operating parameter value and/or a communication based on the operating parameter value.

Determining an operating envelope for controlling equipment

Example implementations involve fault detection and isolation in industrial networks through defining a component as a combination of measurements and parameters and define an industrial network as a set of components connected with different degrees of connections (weights). Faults in industrial network are defined as unpermitted changes in component parameters. Further, the fault detection and isolation in industrial networks are formulated as a node classification problem in graph theory. Example implementations detect and isolate faults in industrial networks through 1) uploading/learning network structure, 2) detecting component communities in the network, 3) extracting features for each community, 4) using the extracted features for each community to detect and isolate faults, 5) at each time step, based on the faulty components provide maintenance recommendation for the network.

System and method for maintenance recommendation in industrial networks

Example implementations described herein are directed to a system for manufacturing dispatching using reinforcement learning and transfer learning. The systems and methods described herein can be deployed in factories for manufacturing dispatching for reducing job-due related costs. In particular, example implementations described herein can be used to reduce massive data collection and reduce model training time, which can eventually improve dispatching efficiency and reduce factory cost.

System for manufacturing dispatching using deep reinforcement and transfer learning

Example implementations described herein involve systems and methods for generating an ensemble of deep learning or neural network models, which can involve, for a training set of data, generating a plurality of model samples for the training set of data, the plurality of model samples generated from deep learning or neural network methods; and aggregating output of the model samples to generate an output of the ensemble models.

Method for reproducibility of deep learning classifiers using ensembles

In some examples, a computer system may receive historical repair data and may extract features from the historical repair data for use as training data. The computer system may determine, from the historical repair data, a repair hierarchy including a plurality of repair levels which includes repair actions as one of the repair levels. Furthermore, the computer system may train the machine learning model, which performs multiple tasks for predicting values of individual levels of the repair hierarchy, by tuning parameters of the machine learning model using the training data.

Equipment repair management and execution

Example implementations described herein involve a new data-driven analytical redundancy relationship (ARR) generation for fault detection and isolation. The proposed solution uses historical data during normal operation to extract the data-driven ARRs among sensor measurements, and then uses them for fault detection and isolation. The proposed solution thereby does not need to rely on the system model, can detect and isolate more faults than traditional data-driven methods, can work when the system is not fully observable, and does not rely on a vast amount of historical fault data, which can save on memory storage or database storage. The proposed solution can thereby be practical in many real cases where there are data limitations.

System and method for data-driven analytical redundancy relationships generation for early fault detection and isolation with limited data

In example implementations described herein, there are systems and methods for processing sensor data from an equipment over a period of time to generate sensor time series data; processing the sensor time series data in a kernel weight layer configured to generate weights to weigh the sensor time series data; providing the weighted sensor time series data to fully connected layers configured to conduct a correlation on the weighted sensor time series data with predictive maintenance labels to generate an intermediate predictive maintenance label; and providing the intermediate predictive maintenance label to an inversed kernel weight layer configured to inverse the weights generated by the kernel weight layer, to generate a predictive maintenance label for the equipment.

Predictive maintenance system for spatially correlated industrial equipment

Example implementations involve a system for Predictive Maintenance using Generative Adversarial Networks for Failure Prediction. Through utilizing three processes concurrently and training them iteratively with data-label pairs, example implementations described herein can thereby generate a more accurate predictive maintenance model than that of the related art. Example implementations further involve shared networks so that the three processes can be trained concurrently while sharing parameters with each other.

System for predictive maintenance using generative adversarial networks for failure prediction

Example implementations described herein are directed to a system for lean angle estimation without requiring specialized calibration. In example implementations, the mobile device sensor data can be utilized without any additional specialized data or configuration to estimate the lean angle of a motorcycle. The lean angle is determined based on a determination of a base attitude of a mobile device and a measured attitude of the mobile device.

Method and system for lean angle estimation of motorcycles

Example implementations described herein are directed to systems and methods for estimating the remaining useful life of a component or equipment through the application of models for deriving functions that can express the remaining useful life over time. In an aspect, the failure acceleration time point is determined for a given type of component, and a function is derived based on the application of models on the failure acceleration time point.

Deep long short term memory network for estimation of remaining useful life of the components

Example implementations described herein involve, for data having incomplete labeling to generate a plurality of predictive maintenance models, processing the data through a multi-task learning (MTL) architecture including generic layers and task specific layers for the plurality of predictive maintenance models configured to conduct tasks to determine outcomes for one or more components associated with the data, each task specific layer corresponding to one of the plurality of predictive maintenance models; the generic layers configured to provide, to the task specific layers, associated data to construct each of the plurality of predictive maintenance models; and executing the predictive maintenance models on subsequently recorded data.

Multi task learning with incomplete labels for predictive maintenance

Growing community of inventors

Austin, TX, United States of America

Chetan Kumar Gupta