Application No.: l l/987,44O
`Examiner: Bharadwaj, Kalpana
`Art Unit: 2129
`
`AMENDMENT TO THE CLAIMS
`
`1. (Currently Amended) 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.
`
`
`Examiner: Bharadwaj, Kalpana
`Art Unit: 2129
`
`AMENDMENT TO THE CLAIMS
`
`1. (Currently Amended) 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.
`
`
`
`Application No.: l l/987,44O
`Examiner: Bharadwaj, Kalpana
`Art Unit: 2129
`
`2. (Original) 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. (Original) 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. (Original) The device for overall machine tool monitoring of claim 1, wherein the
`
`first and the second neural networks are competitive learning neural networks.
`
`5. (New) 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 amounts of
`
`characteristics having a plurality of parameters from the target signal;
`
`a first and a second neural networks for classifying the amounts 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,
`
`amounts of characteristics extracted from a target signal generated when the machine
`
`tool is racing prior to machining a workpiece,
`
`wherein the second neural network classifies,
`
`into normal and abnormal
`
`categories, amounts of characteristics extracted from a target signal generated while the
`
`machine tool is machining the workpiece,
`
`wherein the determination unit determines whether or not an anomaly in an
`
`attachment state of a tool exists before the machine tool machines the workpiece based
`
`on the classification results from the first neural network and whether or not an anomaly
`
`-3-
`
`
`
`Application No.: l l/987,44O
`Examiner: Bharadwaj, Kalpana
`Art Unit: 2129
`
`in a contact state of the tool to the workpiece exists_while the machine tool is machining
`
`the workpiece based on the classification results from the second neural network, and
`
`whether or not there is a fault in the machine tool itself, based on the classification
`
`results from the first and the second neural networks,
`
`first deviation which is a
`
`normalized value of a magnitude of the difference vector 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 second deviation
`
`which is a normalized value of a magnitude of the difference vector 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, and
`
`wherein the determination unit determines that there exists the fault
`
`in the
`
`machine tool if one of the first and the second deviation is greater than a preset
`
`threshold.
`
`6. (New) The device for overall machine tool monitoring of claim 5, the target signal is
`
`output of a vibration sensor attached to the machine tool.
`
`7. (New) The device for overall machine tool monitoring of claim 5, 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.
`
`8. (New) The device for overall machine tool monitoring of claim 5, wherein the first
`
`and the second neural networks are competitive learning neural networks.
`
`
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