Monitoring diagnostic apparatus using neural network

• US 5,419,197 A
• Filed: 03/10/1993
• Issued: 05/30/1995
• Est. Priority Date: 06/02/1992
• Status: Expired due to Fees

• Current Assignee: Mitsubishi Denki Kabushiki Kaisha (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Denki Kabushiki Kaisha (Mitsubishi Electric Corporation)
• Inventors: Akimoto, Yoshiakira, Tanaka, Hideo, Izui, Yoshio, Ogi, Hiromi
• Primary Examiner(s): Williams, Hezron E.
• Assistant Examiner(s): Finley, Rose M.
• Application Number:
  US08/028,822
• Time in Patent Office: 811 Days
• Field of Search: 307/201, 307/464, 364/550, 364/551.01, 395/21, 395/22, 395/23, 395/24, 73/587, 73/658, 73/659
• US Class Current: 73/659
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• CPC Class Codes: G05B 23/0281 Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis
• CPC Class Codes: H02H 1/0023 sensing non electrical parameters, e.g. by optical, pneumatic, thermal or sonic sensors
• CPC Class Codes: H02H 1/0092 concerning the data processing means, e.g. expert systems, neural networks
• CPC Class Codes: H02H 7/22 for distribution gear, e.g. bus-bar systems; for switching devices detecting mechanical or electrical defects in gas-insulated switchgears H02B13/065

Abstract

A monitoring diagnostic apparatus for detecting an abnormality occurring in an object being monitored such as electrical equipment and determining the cause of the abnormality is disclosed. First of all, vibration or partial discharge occurring in the monitored object is detected by using a sensor installed in close proximity to the monitored object. A detection signal output by the sensor then undergoes predetermined signal processing such as the Fourier transform and normalization. After the predetermined signal processing is completed, a neural network identifies the abnormality occurring in the monitored object and determining the cause of the abnormality. The neural network carries out a learning process based on causes of abnormalities occurring in the monitored object, outputting signals corresponding to the causes of the abnormalities.

Claims

1. A monitoring diagnostic apparatus for detecting abnormalities occurring in a monitored object comprising:

an acceleration sensor installed at said monitored object for measuring an acceleration of the monitored object resulting from vibration occurring in said monitored object and outputting an acceleration signal representing said acceleration;

a signal processing means for executing predetermined signal processing on said acceleration signal;

a neural network for identifying an abnormality and determining an abnormality cause thereof by inputting a signal undergoing said signal processing at said signal processing means;

wherein said neural network comprises a plurality of neural elements constituting an input layer, a plurality of neural elements constituting an output layer and connectivity weights connecting said input-layer neural elements to said output-layer neural elements, and wherein said input-layer neural elements are used for inputting signals generated by said signal processing means;

wherein the values of said connectivity weights of said neural network are determined in a learning process so that a specific output-layer neural element produces a maximum output corresponding to an already known cause of an abnormality occurring in said monitored object; and

wherein in the event of an abnormality occurring in said monitored object whose cause is not known in advance, said neural network undergoes a learning process for self-creating a new output-layer neural element and determining the values of said connectivity weights so that said new output-layer neural element produces a maximum output.