Gerald M. Knapp
Engineering at Glenmore Ave, Baton Rouge, LA
License number
Louisiana PE.0027137
Issued Date
Feb 11, 1997
Expiration Date
Sep 30, 2017
Category
Civil Engineer
Type
Industrial Engineer
Address
Address
1765 Glenmore Ave, Baton Rouge, LA 70808
Professional information
Supervised Training Of A Neural Network
US Patent:
5566273, Oct 15, 1996
Filed:
Dec 30, 1993
Appl. No.:
8/176458
Inventors:
Hsin-Hao Huang - Kaohsiung, TW
Gerald M. Knapp - Baton Rouge LA
Gerald M. Knapp - Baton Rouge LA
Assignee:
Caterpillar Inc. - Peoria IL
International Classification:
G06K 962, G06K 900
US Classification:
395 23
Abstract:
The present invention provides a system and method for supervised training of a neural network. A neural network architecture and training method is disclosed that is a modification of an ARTMAP architecture. The modified ARTMAP network is an efficient and robust paradigm which has the unique property of incremental supervised learning. Furthermore, the modified ARTMAP network has the capability of removing undesired knowledge that has previously been learned by the network.
Machine Fault Diagnostics System And Method
US Patent:
5566092, Oct 15, 1996
Filed:
Dec 30, 1993
Appl. No.:
8/176482
Inventors:
Hsin-Hao Huang - Kaohsiung, TW
Gerald M. Knapp - Baton Rouge LA
Chang-Ching Lin - Tallahassee FL
Julie K. Spoerre - Tallahassee FL
Gerald M. Knapp - Baton Rouge LA
Chang-Ching Lin - Tallahassee FL
Julie K. Spoerre - Tallahassee FL
Assignee:
Caterpillar Inc. - Peoria IL
International Classification:
G01B 700
US Classification:
36455102
Abstract:
The invention provides a machine fault diagnostic system to help ensure effective equipment maintenance. The major technique used for fault diagnostics is a fault diagnostic network (FDN) which is based on a modified ARTMAP neural network architecture. A hypothesis and test procedure based on fuzzy logic and physical bearing models is disclosed to operate with the FDN for detecting faults that cannot be recognized by the FDN and for analyzing complex machine conditions. The procedure described herein is able to provide accurate fault diagnosis for both one and multiple-fault conditions. Furthermore, a transputer-based parallel processing technique is used in which the FDN is implemented on a network of four T800-25 transputers.