Neural network state diagnostic system for equipment

A state diagnostic system for a rotating machine, comprising:

• a neural network model for learning in advance at least one sample of time dependent waveforms of vibrations, which are produced in abnormal and normal operation states of the rotating machine, in association with the corresponding operation state, and producing an output signal corresponding to diagnostic results when data related to time dependent waveforms of vibrations produced upon-operation of the rotating machine are inputted;
• means for inputting to the neural network model the data related to time dependent waveforms of vibrations produced upon operation of the rotating machine;
• learning means for successively feeding the one or more samples of time dependent waveforms of vibrations, which are produced in abnormal and normal operation states of the rotating machine, to the neural network model and causing the neural network model to learn to obtain a different output signal for each operation state;
• and
• output means for indicating the state of learning of the neural network model.

A method for diagnosing a state of operation of a rotating machine using a state diagnostic system, said system having a neural network model capable of diagnosing a state of operation of the rotating machine, comprising the steps of:

• learning one or more samples of time dependent waveforms of vibrations, which are produced in abnormal and normal operation states of the rotating machine, in association with the corresponding operation state, to the neural network model;
• inputting data related to time dependent waveforms of vibrations, produced in an operation state of the rotating machine, to the neural network model to obtain an output signal corresponding to diagnostic results;
• and
• converting the output signal from the neural network model into a message to a user and outputting the message as at least a part of diagnostic results.

A state diagnostic system for a rotating machine, said system being adapted to detect a state of vibrations produced upon operation of the rotating machine and to diagnose a state of the operation of the rotating machine on the basis of detected time dependent waveform information, said system comprising:

• a data conversion unit for converting the detected time dependent waveform information, from the rotating machine, to time dependent abnormality monitoring data;
• a neural network model for learning from samples of the detected time dependent waveform information, said neural network model determining the presence or absence of an abnormality and details of the abnormality by a position of an output signal which appears at an output unit upon input of the time dependent abnormality monitoring data;
• means for converting said output signal from said neural network model into a message and for outputting the message as at least a part of diagnostic results;
• and
• a man-machine unit having a screen for displaying said message.

A state diagnostic system for a rotating machine, said system being adapted to detect a state of vibrations produced upon operation of the rotating machine and to diagnose a state of the operation of the rotating machine on the basis of detected time dependent waveform information, said system comprising:

• a data conversion unit for converting detected time dependent waveform information, from the rotating machine, to time dependent abnormality monitoring data;
• a neural network model for learning from samples of the detected time dependent waveform information, said neural network model being capable of determining the presence or absence of an abnormality and details of the abnormality by a position of an output signal which appears at an output unit upon input of the time dependent abnormality monitoring data;
• a learning sample storage unit for storing, for learning purpose, the samples of the detected time dependent waveform information;
• a learning control unit for causing the neural network model to learn the samples stored in the learning sample storage unit;
• and
• a learning man-machine unit for indicating progress of results of the learning of the samples to a user.

A system for previewing/predicting an abnormality in the operation of a rotating machine, said system being adapted to detect vibrations produced upon operation of the rotating machine and to preview/predict an occurrence of abnormality in the rotating machine on the basis of detected time dependent waveform information, said system comprising:

• a data conversion unit for converting detected time dependent waveform information, from the rotating machine, to time dependent abnormality monitoring data;
• a neural network model for learning samples of the time dependent waveform information, said neural network model previewing/predicting an abnormality in accordance with a position of a signal which appears at an output unit upon input of the time dependent abnormality monitoring data;
• and
• a diagnostic man-machine unit for providing a user with results of the preview/predict as output information from the neural network model the man-machine unit having a screen for displaying diagnostic results, and the results of the preview/predict to be displayed on the screen including at least one of information describing phenomenon, information describing the cause, or information on a counter-measure guidance.

A diagnosis training system for training the diagnosis of a state of operation of a rotating machine, said system comprising:

• a neural network model for determining the presence or absence of an abnormality and details thereof on the basis of a position of a signal at an output unit upon input of time dependent abnormality monitoring data on a vibration phenomenon of the rotating machine as a target of the training of the diagnosis;
• an assumed abnormality producing unit for setting, as the time dependent abnormality monitoring data, time dependent waveform information from the rotating machine and corresponding to various assumed abnormality causes of the rotating machine at an input portion of the neural network model;
• and
• a man-machine unit for indicating, to a user, diagnosis results which appear at the output unit when the data generated from the assumed abnormality producing unit are inputted in the neural network model.

A service life estimation assisting system for detecting time dependent waveforms of vibrations produced upon operation of a rotating machine and assisting in estimation of the service life of the rotating machine on the basis of detected time dependent waveform information, which comprises:

• a data conversion unit for converting detected time dependent waveform information, from the rotating machine, to service life estimating data;
• a neural network model for learning samples of various time dependent waveform information, said neural network model estimating the service life of the rotating machine on the basis of the magnitude of a signal which appears at an output unit upon input of the service life estimating data;
• and
• a service life estimating man-machine unit for providing a user with results of the service life estimation as output information from the neural network model.

A state diagnostic system for equipment, comprising:

• a neural network model for learning in advance one or more samples of time dependent waveforms of vibrations, which are produced in abnormal and normal operation states of the equipment, in association with the corresponding operation state, and producing an output signal corresponding to diagnostic results when data related to time dependent waveforms of vibrations produced upon operation of the equipment is inputted;
• means for inputting to the neural network model the data related to time dependent waveforms of vibrations produced upon operation of the equipment;
• means for converting the output signal from the neural network model into a message to a user and outputting the message as at least a part of diagnostic results;
• and `a man-machine unit having a screen for displaying said message.

A state diagnostic system for equipment, comprising:

• a neural network model for learning in advance at least one sample of time dependent waveforms of vibrations, which are produced in abnormal and normal operation states of the equipment, in association with the corresponding operation state, and producing an output signal corresponding to diagnostic results when data related to time dependent waveforms of vibrations produced upon-operation of the equipment is inputted;
• means for inputting to the neural network model the data related to time dependent waveforms of vibrations produced upon operation of the equipment;
• learning means for successively feeding the at least one sample of the time dependent waveforms of vibrations, which are produced in abnormal and normal operation states of the equipment, to the neural network model and causing the neural network model to learn to obtain a different output signal for each operation state;
• and
• output means for indicating the state of learning of the neural network model.

A method for diagnosing a state of operation of equipment using a state diagnostic system, said system having a neural network model for diagnosing a state of operation of the equipment, comprising the steps of:

• learning one or more samples of time dependent waveforms of vibrations, which are produced in abnormal and normal operation states of the equipment, in association with the corresponding operation state, to the neural network model;
• inputting data related to the time dependent waveforms of vibrations, produced in an operation state of the equipment, to the neural network model to obtain an output signal corresponding to diagnostic results;
• and
• converting the output signal from the neural network model into a message to a user and outputting the message as at least a part of diagnostic results.

A state diagnostic system for equipment, said system being adapted to detect time dependent waveform vibration information produced upon operation of the equipment and to diagnose a state of the operation of the equipment on the basis of detected time dependent waveform vibration information, said system comprising:

• a data conversion unit for converting the detected time dependent waveform vibration information, from the equipment, to time dependent abnormality monitoring data;
• a neural network model for learning samples of the detected time dependent waveform information, said neural network model determining the presence or absence of an abnormality and details of the abnormality by a position of an output signal which appears at an output unit upon input of the time dependent abnormality monitoring data;
• means for converting said output signal from said neural network model into a message and for outputting the message as at least a part of diagnostic results;
• and
• a man-machine unit having a screen for displaying said message.

A state diagnostic system for equipment, said system being adapted to detect time dependent waveform vibration information produced upon operation of the equipment to diagnose a state of the operation of the equipment on the basis of detected time dependent waveform vibration information, said system comprising:

• a data conversion unit for converting detected time dependent waveform vibration information, from the equipment, to time dependent abnormality monitoring data;
• a neural network model for learning samples of the detected time dependent waveform vibration information, said neural network model determining the presence or absence of an abnormality and details of the abnormality by a position of an output signal which appears at an output unit upon input of the time dependent abnormality monitoring data;
• a learning sample storage unit for storing, for learning purpose, samples of the detected time dependent waveform vibration information;
• a learning control unit for causing the neural network model to learn the samples stored in the learning sample storage unit;
• and
• a learning man-machine unit for indicating progress of results of the learning of the samples to a user.

A system for previewing/predicting an abnormality in the operation of equipment, said system being adapted to detect time dependent waveform vibration information produced upon operation of the equipment and to preview/predict an occurrence of abnormality in the equipment on the basis of detected time dependent waveform vibration information, said system comprising:

• a data conversion unit for converting detected time dependent waveform vibration information, from the equipment, to time dependent abnormality monitoring data;
• a neural network model for learning samples of various abnormal time dependent waveform vibration information, said neural network model previewing/predicting an abnormality in accordance with a position of a signal which appears at an output unit upon input of the time dependent abnormality monitoring data;
• and
• a diagnostic man-machine unit for providing a user with results of the preview/predict as output information from the neural network model the man-machine unit having a screen for displaying diagnostic results, and the results of the preview/predict to be displayed on the screen including at least one of information describing phenomenon, information describing the cause, or information on a counter-measure guidance.

A diagnosis training system for training the diagnosis of a state of operation of equipment, said system comprising:

• a neural network model for determining the presence or absence of an abnormality and details thereof on the basis of a position of a signal at an output unit upon input of time dependent abnormality monitoring data of a vibration phenomenon of the equipment as a target of the training of the diagnosis;
• an assumed abnormality producing unit for setting, as the time dependent abnormality monitoring data, time dependent waveform information from the equipment and corresponding to various assumed abnormality causes of the equipment, at an input portion of the neural network model;
• and
• a man-machine unit for indicating, to a user, diagnosis results which appear at the output unit when the data generated from the assumed abnormality producing unit are inputted in the neural network model.

A service life estimation assisting system for detecting time dependent waveform vibration information produced upon operation of equipment and assisting in estimation of the service life of the equipment on the basis of detected time dependent waveform vibration information, said system comprising:

• a data conversion unit for converting detected time dependent waveform vibration information, related to data from the equipment, to service life estimating data;
• a neural network model for learning samples of time dependent waveform vibration information, said neural network model estimating the service life of the equipment on the basis of the magnitude of a signal which appears at an output unit upon input of the service life estimating data;
• and
• a service life estimating man-machine unit for providing a user with results of the service life estimation as output information from the neural network model.