Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Technical Center, U.s.a., Inc.

University of Connecticut

Liu Qiao

Danil V Prokhorov

Krishna Pattipatti

Shunsuke Chigusa

Setu Madhavi Namburu

Sandesh Ghimire

Jianhui Luo

Masayuki Kawmamoto

An adaptive information processing system for updating product documentation and associated knowledge base is disclosed, the system including at least one subsystem for receiving original data from a data source, and a central dynamic data system to integrate the original data from the at least one subsystem. The central dynamic data system is configured to integrate system knowledge with the original data to form integrated data, wherein the central dynamic data system is configured to dynamically update the product documentation and the knowledge base based on the integrated data. A computer implemented method for dynamically updating product documentation and knowledge base is further disclosed, the method includes receiving original data from a data source, and integrating the knowledge base with the original data from the data source to form integrated data. The method further includes mapping the integrated data with at least one application and dynamically updating product documentation and the knowledge base based on the integrated data.

Adaptive information processing systems, methods, and media for updating product documentation and knowledge base

A system for determining a most probable cause or causes of a problem in a plant is disclosed. The system includes a plant, the plant having a plurality of subsystems that contribute to the operation of the plant, the plurality of subsystems having operating functions that produce operational signals. A plurality of sensors that are operable to detect the operational signals from the plurality of subsystems and transmit data related to the signals is also provided. An advisory system is disclosed that receives an input, the input being in the form of data from the plurality of sensors, possible input root causes of the problem, possible input symptoms of the problem and/or combinations thereof. The advisory system has an autoencoder in the form of a recurrent neural network. The recurrent neural network has sparse connectivity in a plurality of nodes, and the autoencoder is also operable to receive the input and perform multiple iterations of computations at each of the plurality of nodes as a function of the input and provide an output. The output can be in the form of possible output causes of the problem, possible output symptoms of the problem and/or combinations thereof.

System for determining most probable cause of a problem in a plant

The present invention discloses a plant diagnostic system for diagnosing a problem with the plant. The plant diagnostic system can include an agent-based plant diagnostic network that has an adaptive global agent located in a central facility, a plant expert agent located at the plant and a plurality of subsystem resident agents. Each of the subsystem resident agents can be assigned to a subsystem of the plant. A diagnosis agent can also be included, the diagnosis agent operable to be instructed by the adaptive global agent, transmitted to the plant expert agent, be received by the plant expert agent, transmitted by the plant expert agent back to the adaptive global agent and be received by the adaptive global agent.

Diagnosis agent for remote plant diagnosis

Systems and methods are provided for monitoring, diagnosis and condition-based maintenance of mechanical systems. The disclosed systems and methods employ intelligent model-based diagnostic methodologies to effectuate such monitoring, diagnosis and maintenance. According to exemplary embodiments of the present disclosure, the intelligent model-based diagnostic methodologies combine or integrate quantitative (analytical) models and graph-based dependency models to enhance diagnostic performance. The disclosed systems and methods may be employed a wide variety of applications, including automotive, aircraft, power systems, manufacturing systems, chemical processes and systems, transportation systems, and industrial machines/equipment.

Intelligent model-based diagnostics for system monitoring, diagnosis and maintenance

The present invention provides a multi-level model-based intelligent agent diagnosis system and method for computer-controlled machinery operative to reduce the complexity typically associated with conventional model based diagnostic systems. The system utilizes a plurality of intelligent agents arranged in a plurality of physically hierarchical layers such that the tasks associated with accomplishing model based diagnosis are distributed amongst the intelligent agents if each layer wherein information gathered from a first lower level intelligent agents is processed by at least one other higher level to realize system fault diagnosis. The system provides increased processing speed of modeling and/or model identification such that faster and more accurate failure isolation and identification is accomplished.

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Ann Arbor, MI, United States of America

Liu Qiao