Method for monitoring an electric motor and detecting a departure from normal operation

• US 5,675,497 A
• Filed: 05/22/1996
• Issued: 10/07/1997
• Est. Priority Date: 06/30/1994
• Status: Expired due to Fees

• Current Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Original Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Inventors: Petsche, Thomas, Garrett, Charles
• Primary Examiner(s): Trammell, James P.
• Application Number:
  US08/651,220
• Time in Patent Office: 503 Days
• Field of Search: 364/485, 364/483, 364/492, 364/494, 395/902-907, 395/912-915, 361/24, 361/25, 361/30, 361/31, 361/5, 318/635, 318/434, 318/806, 324/772
• US Class Current: 702/182
• CPC Class Codes: G01R 31/343 in operation
• CPC Class Codes: G06F 18/2414 Smoothing the distance, e.g. radial basis function networks [RBFN]
• CPC Class Codes: G06F 2218/12 Classification; Matching
• CPC Class Codes: G06N 3/049 Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• CPC Class Codes: G06N 3/065 Analogue means
• CPC Class Codes: H02H 1/0092 concerning the data processing means, e.g. expert systems, neural networks
• CPC Class Codes: H02H 7/085 against excessive load H02H6/00 takes precedence
• CPC Class Codes: Y10S 706/911 Nonmedical diagnostics
• CPC Class Codes: Y10S 706/912 Manufacturing or machine, e.g. agricultural machinery, machine tool

Abstract

A method for detecting a departure from normal operation of an electric motor comprises the steps of modelling a set of normal current measurements and a set of operational current measurements for the motor being monitored. The modelling is carried out by a neural network auto-associator which is trained to reproduce its inputs on its output. A potential failure is indicated whenever the set of normal current measurements and the set of operational current measurements differ by more than a predetermined criterion.

Claims

1. A method for detecting a departure from normal operation of an electric motor, comprising:

modelling a set of normal current measurements for a motor being monitored;

modelling a set of operational current measurements for said motor being monitored, wherein said modelling is carried out by a neural network auto-associator which is trained to reproduce its inputs on its output;

indicating a potential failure whenever said set of normal current measurements and said set of operational current measurements differ by more than a predetermined criterion.

2. A method for detecting a departure from normal operation of an electric motor in accordance with claim 1 wherein new sets of current measurements are classified as "good" or "bad" by first transforming the measurement using a Fast Fourier Transform (FFT) and an internal scaling procedure, and then applying a subset of the transformed measurements as inputs to the neural network auto-associator, whereby a decision is generated based on the difference between the input and output of the network.

3. A method for detecting a departure from normal operation of an electric motor in accordance with claim 2 wherein said neural network auto-associator is trained utilizing current measurements collected using an electric motor known to be in a normal operating condition.

4. A method for detecting a departure from normal operation of an electric motor in accordance with claim 3 wherein said subset of the transformed measurements is user-definable.