Abnormality detection system and abnormality detection program

Patent metadata

Field	Value
Publication number	US-2020410363-A1
Application number	US-202016855763-A
Country	US
Kind code	A1
Filing date	Apr 22, 2020
Priority date	Jun 28, 2019
Publication date	Dec 31, 2020
Grant date	—

Abstract

An abnormality detection system has a detection target waveform generation unit and a detection target waveform determination/abnormality detection unit. The detection target waveform includes a target waveform detection algorithm learning the detection target waveform and generates an expected detection target waveform by executing the target waveform detection algorithm for an input waveform. The detection target waveform determination/abnormality detection unit compares the expected detection target waveform with the input waveform to determine that the input waveform corresponds to the detection target waveform.

First claim

What is claimed is:

1 . An abnormality detection system, comprising:
a detection target waveform generation unit having an target waveform detection algorithm learning a detection target waveform, being configured to generate an expected detection target waveform by executing the target waveform detection algorithm for an input waveform included in an input signal, and being configured to output the expected detection target waveform; a detection target waveform determination and abnormality detection unit configured to compare the expected detection target waveform with the input waveform and to determine that the input waveform corresponds to the detection target waveform.

2 . The abnormality detection system according to claim 1 ,
wherein the detection target waveform determination and abnormality detection unit compares the input waveform determined to correspond to the detection target waveform with the expected detection target waveform to determine whether the input waveform is normal or abnormal.

3 . The abnormality detection system according to claim 2 ,
wherein the target waveform detection algorithm is an autoencoder using a neural network.

4 . The abnormality detection system according to claim 4 ,
wherein the autoencoder includes a plurality of input nodes, a plurality of output nodes whose number is the same as the number of the input nodes, and a plurality of hidden nodes whose number is lower than the number of the input nodes, wherein a plurality of values obtained at a predetermined time interval along time series from the input waveform are input to the input nodes.

5 . The abnormality detection system according to claim 1 ,
wherein the detection target waveform generation unit outputs the detection target waveform learnt by the target waveform detection algorithm as the expected detection target waveform.

6 . The abnormality detection system according to claim 6 ,
wherein the detection target waveform determination and abnormality detection unit determines by using likelihood between the expected detection target waveform and the input waveform, determines whether the input waveform includes the detection target waveform by using a first threshold of the likelihood, and determines whether the input waveform is normal by using a second threshold of the likelihood, and wherein the first threshold is lower than the second threshold.

7 . The abnormality detection system according to claim 6 ,
wherein the likelihood is calculated based on Euclidean distance.

8 . The abnormality detection system according to claim 1 , further comprising:
a signal input unit configured to receive a monitor signal from a detection target, and an input signal buffer configured to hold the monitor signal within a predetermined period from the signal input unit to output to the detection target waveform generation unit and the detection target waveform determination and abnormality detection unit as the input signal including the input waveform.

9 . The abnormality detection system comprising:
a detection target waveform partial generation unit having an trigger detection algorithm which learnt a detection target waveform, being configured to generate a part of an expected detection target waveform by executing the trigger detection algorithm for an input waveform included in an input signal, and being configured to output the part of the expected detection target waveform; a detection target waveform determination unit configured to compare the part of the expected detection target waveform with a part of the input waveform and to determine that the part of the input waveform corresponds to the part of the detection target waveform.

10 . The abnormality detection system according to claim 9 , further comprising:
a detection target waveform generation unit having an target waveform detection algorithm which learnt the detection target waveform, being configured to generate the expected detection target waveform by executing the target waveform detection algorithm for the input waveform having the part of the input waveform determined to corresponding to the part of the detection target waveform, and being configured to output the expected detection target waveform, and an abnormality detection unit configured to compare the expected detection target waveform with the input waveform and to determine that the input waveform is normal or abnormal.

11 . The abnormality detection system according to claim 10 ,
wherein the target waveform detection algorithm is autoencoder using a neural network.

12 . The abnormality detection system according to claim 10 ,
wherein the detection target waveform determination unit and abnormality detection unit determine by using likelihood between the expected detection target waveform and the input waveform, wherein the detection target waveform determination unit determines by using a third threshold of the likelihood, wherein the abnormality detection unit determines by using a fourth threshold of the likelihood, wherein the third threshold is lower than the fourth threshold.

13 . The abnormality detection system according to claim 1 , further comprising:
a first manufacturing apparatus and a second manufacturing apparatus, an algorithm storage unit storing a first target waveform detection algorithm corresponding to a manufacturing condition of the first manufacturing apparatus and a second target waveform detection algorithm corresponding to a manufacturing condition of the second manufacturing apparatus, wherein the input signal is provided from the first manufacturing apparatus or the second manufacturing apparatus which is a detection target apparatus, and wherein the detection target waveform generation unit selects the first target waveform detection algorithm or the second target waveform detection algorithm.

14 . A programmable storage medium storing an abnormality detection program to be operated on a computer, the program comprising the steps of:
generating an expected detection target waveform by executing a target waveform detection algorithm, which is learnt a detection target waveform, for the input waveform included in an input signal to output the expected detection target waveform, and determining whether the input waveform includes the detection target waveform by comparing the expected detection target waveform with the input waveform.

15 . The programmable storage medium according to claim 14 ,
wherein the program further comprises
determining whether the input waveform is normal or abnormal by comparing the input waveform determined to include the detection target waveform with the expected detection target waveform.

16 . The programmable storage medium according to claim 15 ,
wherein the target waveform detection algorithm is an autoencoder using a neural network.

17 . The programmable storage medium according to claim 16 ,
wherein the autoencoder includes a plurality of input nodes, a plurality of output nodes whose number is the same as the number of the input nodes, and a plurality of hidden nodes whose number is lower than the number of the input nodes, wherein a plurality of values obtained at a predetermined time interval along time series from the input waveform are input to the input nodes.

18 . The programmable storage medium according to claim 15 ,
wherein the determining whether the input waveform includes the detection target waveform based on a first threshold of a likelihood between the expected detection targe

Assignees

• Renesas Electronics Corp

Inventors

• Kawatake Masatoshi

Classifications

• H10P72/0604

  Process monitoring, e.g. flow or thickness monitoring · CPC title

• G06F18/241

  relating to the classification model, e.g. parametric or non-parametric approaches · CPC title

• G06N3/048

  Activation functions · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06F2218/12

  Classification; Matching · CPC title