General Electric Company

Vipin Kewal Ramani

Rasiklal Punjalal Shah

Yu-To Chen

Piero Patrone Bonissone

Phillip Edward Steen

John Andrew Johnson

Ramesh Ramachandran

Randal Vincent Dusing

Peter Michael Edic

Anil Varma

Abdalmajeid Musa Alyassin

Nicolas Wadih Chbat

Shankar Visvanathan Guru

Sharon Shihua Liu

Christopher James Dailey

Susan Teeter Wallenslager

Ramesh Ramchandran

A method and system for canceling noise and compressing data from a motor phase angle sensor. In this invention, phase angle data is sampled at a first sampling rate to generate a sampled phase angle. The sampled phase angle signal is sub-sampled at a second sampling rate to generate a sub-sampled phase angle signal which is transformed by subtracting each of the sub-sampled data points of the sub-sampled signal from 90 degrees to generate a transformed signal. The transformed phase angle signal is then convoluted with a wavelet signal to generate a convoluted phase angle signal. Next, a phase angle range signal is generated by calculating the range between the largest and smallest convoluted data points within each of a plurality of segments of the convoluted signal. A moving average calculation is performed on the phase angle range signal to generate a moving average signal.

Method and system for canceling noise and compressing data from a motor phase angle sensor

A system and method for diagnosing and validating a machine with waveform data generated therefrom. Historical waveform data are obtained from machines having known faults along with corresponding actions for repairing the machines and are used to develop fault classification rules. The fault classification rules are stored in a diagnostic knowledge database. The database of classification rules are used to diagnose new waveform data from a machine having an unknown fault.

System and method for diagnosing and validating a machine using waveform data

A system and method for diagnosing and validating a machine over a network using waveform data. Historical waveform data are obtained via the network from machines having known faults along with corresponding actions for repairing the machines and are used to develop fault classification rules. The fault classification rules are stored in a diagnostic knowledge database. The database of classification rules are used to diagnose new waveform data from a machine having an unknown fault, via the network.

System and method for diagnosing and validating a machine over a network using waveform data

The present invention is directed to a method for processing records to extract relevant, reusable diagnostic information and make the information available in a form capable of being used by field engineers to speed the diagnosis and repair or adjustment of equipment.

Automated creation of a diagnostic tool for the servicing of equipment from free form text dispatches

This invention provides a system and method for integrating a plurality of diagnostic information from a multiple of sources. In this invention, there is a site specific database which contains site specific information for a machine. A plurality of diagnostic related information is obtained from the machine. A diagnostic router collects the site specific information for the machine and the plurality of diagnostic related information and generates a current incident record therefrom. An approved incident record database contains a plurality of approved incident records obtained from a plurality of machines. An integrator finds approved incident records from the approved incident record database that most closely match the current incident record.

System and method for integrating a plurality of diagnostic related information

A diagnostic system is provided for identifying faults in a machine (e.g., CT scanner, MRI system, x-ray apparatus) by analyzing a data file generated thereby. The diagnostic system includes a trained database containing a plurality of trained data, each trained data associated with one of plurality of known fault types. Each trained data is represented by a trained set of feature values and corresponding weight values. Once a data file is generated by the machine, a current set of feature values are extracted from the data file by performing various analyses (e.g., time domain analysis, frequency domain analysis, wavelet analysis). The current set of feature values extracted is analyzed by a fault detector which produces a candidate set of faults based on the trained set of feature values and corresponding weight values for each of the fault types. The candidate set of faults produced by the fault detector is presented to a user along with a recommend repair procedure. In cases where no fault is identified or in response to a misdiagnosis produced by the diagnostic system, the user may interactively input a faulty condition associated with the machine being diagnosed (e.g., based on his/her experience). The diagnostic system further includes a learning subsystem which automatically updates the plurality of trained data based on the faulty condition input by the user.

Diagnostic system with learning capabilities

A system is provided for identifying faults in a non-OEM computed tomography system having components in connection with the detectors to reconstruct an image. Then, correlation values are derived from baseline reference data files and current data files collected after a fault is suspected in the CT system. The correlation values are compared against a threshold to identify suspect detectors and a pattern recognition algorithm is applied to determine if the suspect detectors are faulty or if one of the components processing the output signals has failed. If baseline files are unavailable, the identification of faulty components is accomplished by performing correlation between the current data files and average profile values.

Method and system for diagnosing faults in imaging scanners

The present invention discloses a system and method for diagnosing and validating a machine with waveform data generated therefrom. In this invention, historical waveform data are obtained from machines having known faults along with corresponding actions for repairing the machines and are used to develop fault classification rules. The fault classification rules are stored in a diagnostic knowledge database. The database of classification rules are used to diagnose new waveform data from a machine having an unknown fault.

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Niskayuna, NY, United States of America

Vipin Kewal Ramani