Abstract: The present invention discloses a method of preparing solar-grade silicon single crystals by using the Czochralski and float-zone process: in the equal-diameter growth process during the float-zone phase, under the control by the electric control system of a float-zone single crystal furnace, a downward-rotating motor alternates forward rotations and reverse rotations; said downward-rotating motor drives silicon single crystals to rotate by the preset forward angle or reverse angle. The present invention improves the radial resistivity variation of solar-grade silicon single crystals and solves the black heart problem with solar-grade silicon single crystals. Thus, the conversion efficiency of the solar cells manufactured using such solar-grade silicon single crystals can be increased.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A battery prognosis system for estimating the remaining useful life of a battery includes a sensor input, a conversion module, and a mapping module. The sensor input is capable of receiving a measurement signal from a sensor measuring properties of the battery. The conversion module is in electronic communication with the sensor input to receive the measurement signal and processes the measurement signal into an output signal of internal parameters of the battery. A mapping model trained on actual battery performance data in the mapping module maps the output signal and time variant parameters related to the output signal to generate a battery life signal corresponding to an estimate of the remaining useful life of the battery.

Abstract: 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.

Abstract: A distributed diagnosis algorithm based on a multi-signal digraph model of an overall system is provided. In addition, a model enables the generation of a fault-test dependency matrix (D-matrix), which summarizes the detection capabilities of tests designed for faults associated with each node. Each row represents a fault state and each column represents a test.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.