Device for overall machine tool monitoring

• US 20080133439A1
• Filed: 11/30/2007
• Published: 06/05/2008
• Est. Priority Date: 11/30/2006
• Status: Abandoned Application

• Current Assignee: Panasonic Electric Works Company Limited (Panasonic Holdings Corporation)
• Original Assignee: Matsushita Electric Industrial Company Limited (Panasonic Holdings Corporation)
• Inventors: Ikeda, Kazutaka
• Application Number:
  US11/987,440
• Publication Number:
  US 20080133439A1
• Time in Patent Office: Days
• US Class Current: 706/20
• CPC Class Codes: G01H 1/003 of rotating machines G01H1/10 takes precedence
• CPC Class Codes: G01H 1/12 of longitudinal or not specified vibrations
• CPC Class Codes: G05B 19/406 characterised by monitoring or safety G05B19/19 takes precedence
• CPC Class Codes: G05B 2219/33296 ANN for diagnostic, monitoring
• CPC Class Codes: G05B 2219/37435 Vibration of machine

Abstract

A first and a second neural network classify, into normal and abnormal categories, amounts of characteristics extracted from target signals generated when a machine tool is racing prior to machining a workpiece and while the machine tool is machining the workpiece, respectively. A determination unit determines whether an anomaly exists before the machine tool machines the workpiece and while the machine tool is machining the workpiece, and whether there is a fault in the machine tool, based on the classification results from the first and the second neural networks, deviation history between weight coefficients of neurons in an output layer included in the first neural network and the amounts of characteristics extracted by the first characteristics extracting unit, and deviation history between weight coefficients of neurons in an output layer included in the second neural network and the amounts of characteristics extracted by the second characteristics extracting unit.

Claims

1. A device for overall machine tool monitoring comprising:

a signal input unit to which a target signal which is an electric signal representing vibrations generated from the machine tool is inputted;

a first and a second characteristics extracting units for extracting an amount of characteristics having a plurality of parameters from the target signal;

a first and a second neural networks for classifying the amount of characteristics extracted by the respective characteristics extracting units into categories; and

a determination unit for determining an overall anomaly in the machine tool by using a classification result from each of the neural networks,

wherein the first neural network classifies, into normal and abnormal categories, an amount of characteristics extracted from a target signal generated when the machine tool is racing prior to machining a workpiece, and

wherein the second neural network classifies, into normal and abnormal categories, the amounts of characteristics extracted from a target signal generated while the machine tool is machining the workpiece, and

wherein the determination unit determines whether or not the anomaly exists before the machine tool machines the workpiece and while the machine tool is machining the workpiece, and whether or not there is a fault in the machine tool, based on the classification results from the first and the second neural networks, deviation history between weight coefficients of neurons in an output layer included in the first neural network and the amounts of characteristics extracted by the first characteristics extracting unit, and deviation history between weight coefficients of neurons in an output layer included in the second neural network and the amounts of characteristics extracted by the second characteristics extracting unit.

2. The device for overall machine tool monitoring of claim 1, the target signal is output of a vibration sensor attached to the machine tool.

3. The device for overall machine tool monitoring of claim 1, wherein the first characteristics extracting unit extracts frequency components from the target signal, and the second characteristics extracting unit extracts a frequency component of an envelop from the target signal.

4. The device for overall machine tool monitoring of claim 1, wherein the first and the second neural networks are competitive learning neural networks.