Monitoring computing system status by implementing a deep unsupervised binary coding network

Patent metadata

Field	Value
Publication number	US-2021065059-A1
Application number	US-202017002960-A
Country	US
Kind code	A1
Filing date	Aug 26, 2020
Priority date	Aug 27, 2019
Publication date	Mar 4, 2021
Grant date	—

Abstract

A computer-implemented method for monitoring computing system status by implementing a deep unsupervised binary coding network includes receiving multivariate time series data from one or more sensors associated with a system, implementing a long short-term memory (LSTM) encoder-decoder framework to capture temporal information of different time steps within the multivariate time series data and perform binary coding, the LSTM encoder-decoder framework including a temporal encoding mechanism, a clustering loss and an adversarial loss, computing a minimal distance from the binary code to historical data, and obtaining a status determination of the system based on a similar pattern analysis using the minimal distance.

First claim

What is claimed is:

1 . A computer-implemented method for monitoring computing system status by implementing a deep unsupervised binary coding network, comprising:
receiving multivariate time series data from one or more sensors associated with a system; implementing a long short-term memory (LSTM) encoder-decoder framework to capture temporal information of different time steps within the multivariate time series data and perform binary coding, the LSTM encoder-decoder framework including a temporal encoding mechanism, a clustering loss and an adversarial loss, wherein implementing the LSTM encoder-decoder framework further includes:
generating one or more time series segments based on the multivariate time series data using an LSTM encoder to perform temporal encoding; and
generating binary code for each of the one or more time series segments based on a feature vector;
computing a minimal distance from the binary code to historical data; and obtaining a status determination of the system based on a similar pattern analysis using the minimal distance.

2 . The method as recited in claim 1 , wherein the one or more time segments are of a fixed window size.

3 . The method as recited in claim 1 , wherein the binary code includes hash code.

4 . The method as recited in claim 1 , wherein the minimal distance is a minimal Hamming distance.

5 . The method as recited in claim 1 , wherein:
the temporal encoding mechanism encodes temporal order of different ones of the one or more time segments within a mini-batch; the clustering loss enhances a nonlinear hidden feature structure; and the adversarial loss enhances a generalization capability of the binary code.

6 . The method as recited in claim 5 , wherein:
the clustering loss is computed based on soft assignments and an auxiliary target distribution; and the adversarial loss is computed based on a generator and a discriminator, the generator being configured to generate a sample feature vector based on a concatenation of a clustering membership, the feature vector and a random noise vector, and the discriminator being configured to distinguish between the sample feature vector and the feature vector.

7 . The method as recited in claim 1 , wherein a full objective of the deep unsupervised binary coding network is computed as a linear combination of the clustering loss, the adversarial loss, and a mean squared error (MSE) loss.

8 . A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method for monitoring computing system status by implementing a deep unsupervised binary coding network, the method performed by the computer comprising:
receiving multivariate time series data from one or more sensors associated with a system; implementing a long short-term memory (LSTM) encoder-decoder framework to capture temporal information of different time steps within the multivariate time series data and perform binary coding, the LSTM encoder-decoder framework including a temporal encoding mechanism, a clustering loss and an adversarial loss, wherein implementing the LSTM encoder-decoder framework further includes:
generating one or more time series segments based on the multivariate time series data using an LSTM encoder to perform temporal encoding; and
generating binary code for each of the one or more time series segments based on a feature vector;
computing a minimal distance from the binary code to historical data; and obtaining a status determination of the system based on a similar pattern analysis using the minimal distance.

9 . The computer program product as recited in claim 8 , wherein the one or more time segments are of a fixed window size.

10 . The computer program product as recited in claim 8 , wherein the binary code includes hash code.

11 . The computer program product as recited in claim 8 , wherein the minimal distance is a minimal Hamming distance.

12 . The computer program product as recited in claim 8 , wherein:
the temporal encoding mechanism encodes temporal order of different ones of the one or more time segments within a mini-batch; the clustering loss enhances a nonlinear hidden feature structure; and the adversarial loss enhances a generalization capability of the binary code.

13 . The computer program product as recited in claim 12 , wherein:
the clustering loss is computed based on soft assignments and an auxiliary target distribution; and the adversarial loss is computed based on a generator and a discriminator, the generator being configured to generate a sample feature vector based on a concatenation of a clustering membership, the feature vector and a random noise vector, and the discriminator being configured to distinguish between the sample feature vector and the feature vector.

14 . The computer program product as recited in claim 8 , wherein a full objective of the deep unsupervised binary coding network is computed as a linear combination of the clustering loss, the adversarial loss, and a mean squared error (MSE) loss.

15 . A system for monitoring computing system status by implementing a deep unsupervised binary coding network, comprising:
a memory device storing program code; and at least one processor device operatively coupled to the memory device and configured to execute program code stored on the memory device to:
receive multivariate time series data from one or more sensors associated with a system;
implement a long short-term memory (LSTM) encoder-decoder framework to capture temporal information of different time steps within the multivariate time series data and perform binary coding, the LSTM encoder-decoder framework including a temporal encoding mechanism, a clustering loss and an adversarial loss, wherein the at least one processing device is further configured to implement the LSTM encoder-decoder framework by:
generating one or more time series segments based on the multivariate time series data using an LSTM encoder to perform temporal encoding; and
generating binary code for each of the one or more time series segments based on a feature vector;

compute a minimal distance from the binary code to historical data; and
obtain a status determination of the system based on a similar pattern analysis using the minimal distance.

16 . The system as recited in claim 15 , wherein the one or more time segments are of a fixed window size.

17 . The system as recited in claim 15 , wherein the binary code includes hash code, and wherein the minimal distance is a minimal Hamming distance.

18 . The system as recited in claim 15 , wherein:
the temporal encoding mechanism encodes temporal order of different ones of the one or more time segments within a mini-batch; the clustering loss enhances a nonlinear hidden feature structure; and the adversarial loss enhances a generalization capability of the binary code.

19 . The system as recited in claim 18 , wherein:
the clustering loss is computed based on soft assignments and an auxiliary target distribution; and the adversarial loss is computed based on a generator and a discriminator, the generator being configured to generate a sample feature vector based on a concatenation of a clustering membership, the feature vector and a random noise vector, and the discriminator being configured to distinguish between the sample feature vector and the feature vector.

Assignees

• Nec Lab America Inc

Inventors

• Song Dongjin
• Chen Yuncong
• Lumezanu Cristian
• Mizoguchi Takehiko
• Chen Haifeng
• Zhu Dixian

Classifications

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/094

  Adversarial learning · CPC title

• G06N3/0495

  Quantised networks; Sparse networks; Compressed networks · CPC title