Machine fault diagnostics system and method
First Claim
1. A fault diagnostic system, comprising:
- (a) a data acquisition module that collects sensory signals;
(b) a diagnostic module, connected to said data acquisition module, that performs on-line fault detection for a physical machine or process, fault diagnostics, and provides recommendations in regard to said on-line fault detection and said fault diagnostics; and
(c) a machine model module, connected to said diagnostic module, that provides a physical model for identifying fault conditions that cannot be diagnosed by said diagnostic module.
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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.
558 Citations
25 Claims
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1. A fault diagnostic system, comprising:
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(a) a data acquisition module that collects sensory signals; (b) a diagnostic module, connected to said data acquisition module, that performs on-line fault detection for a physical machine or process, fault diagnostics, and provides recommendations in regard to said on-line fault detection and said fault diagnostics; and (c) a machine model module, connected to said diagnostic module, that provides a physical model for identifying fault conditions that cannot be diagnosed by said diagnostic module. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A method for diagnosing a physical machine or process, the method comprising the steps of:
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(1) acquiring a first set of data from the physical machine or process; (2) preprocessing said first set of data using an autoregressive model, said preprocessing generating an autoregressive parameter; and (3) detecting abnormal conditions in said autoregressive parameter using an overall root means square (RMS) measurement and a covariance statistic of an exponentially weighted moving average (EWMA), wherein if an abnormal condition is detected then, (a) identifying whether said physical machine or process has a fault, including, (i) determining a hypothesis with the aid of a fault diagnostic network, and if said fault diagnostic network cannot generate a hypothesis, then (ii) determining a hypothesis with the aid of a model-based reasoning approach, wherein said model-based reasoning approach uses fuzzy logic; and (b) supplying said identifiable fault to a fault expert system having a knowledge base with a set of rules, wherein said fault reasoning expert system checks said identifiable fault against said set of rules. - View Dependent Claims (21, 22, 23, 24, 25)
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Specification