Hitachi, Ltd.

Dipanjan Ghosh

Chetan Kumar Gupta

Ahmed Khairy Farahat

Chi Zhang

Maria Teresa Gonzalez Diaz

Michiko Yoshida

Mahbubul Alam

Jiro Hashizume

Kosta Ristovski

Adriano Arantes

Marcos Vieira

Tsubasa Watanabe

Walid Shalaby

Phawis Thammasorn

Lijing Wang

Xian Yeow Lee

Aman Kumar

Lasitha Vidyaratne

Adriano S Arantes

Maria Teresa Gonzales Diaz

Gregory Sin

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

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 defect analysis for images received from a camera system, which can involve applying a first model configured to determine regions of interest of the object from the images, applying a second model configured to identify localized areas of the object based on the regions of interest on the images; and applying a third model configured to identify defects in the localized ones of the images.

AI-based inspection in transportation

Example implementations described herein are directed to systems and methods for predictive maintenance with health indicators using reinforcement learning. An example implementation includes a method to receive sensor data, operational condition data, and failure event data and generate a model to determine health indicators that indicate equipment performance based on learned policies, state values, and rewards. The model is applied to external sensor readings and operating data for a piece of equipment to output a recommendation based on the model.

Automatic health indicator learning using reinforcement learning for predictive maintenance

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Santa Clara, CA, United States of America

Dipanjan Ghosh