Transferring failure samples using conditional models for machine condition monitoring

Patent metadata

Field	Value
Publication number	US-10108513-B2
Application number	US-201415303243-A
Country	US
Kind code	B2
Filing date	Apr 16, 2014
Priority date	Apr 16, 2014
Publication date	Oct 23, 2018
Grant date	Oct 23, 2018

Abstract

A method for predicting failure modes in a machine includes learning ( 31 ) a multivariate Gaussian distribution for each of a source machine and a target machine from data samples from one or more independent sensors of the source machine and the target machine, learning ( 32 ) a multivariate Gaussian conditional distribution for each of the source machine and the target machine from data samples from one or more dependent sensors of the source machine and the target machine using the multivariate Gaussian distribution for the independent sensors, transforming ( 33 ) data samples for the independent sensors from the source machine to the target machine using the multivariate Gaussian distributions for the source machine and the target machine, and transforming ( 34 ) data samples for the dependent sensors from the source machine to the target machine using the transformed independent sensor data samples and the conditional Gaussian distributions for the source machine and the target machine.

First claim

What is claimed is:

1. A computer-implemented method for predicting failure modes in a machine, the method implemented by the computer comprising:
learning a multivariate Gaussian distribution for each of a source machine and a target machine from data samples from one or more independent sensors of the source machine and the target machine, wherein said data samples are acquired under normal operating conditions for each machine;
learning a multivariate Gaussian conditional distribution for each of the source machine and the target machine from data samples from one or more dependent sensors of said source machine and said target machine using the multivariate Gaussian distribution for the independent sensors, wherein said data samples are acquired under normal operating conditions for each machine;
transforming data samples for the independent sensors from the source machine to the target machine using the multivariate Gaussian distributions for the source machine and the target machine;
transforming data samples for the dependent sensors from the source machine to the target machine using the transformed independent sensor data samples and the conditional Gaussian distributions for the source machine and the target machine,
acquiring data samples from the independent sensors of the source machine associated with a failure;
transforming said failure data samples for the independent sensors from the source machine to the target machine using the multivariate Gaussian distributions for the source machine and the target machine; and
transforming said failure data samples for the dependent sensors from the source machine to the target machine using the transformed independent sensor data samples and the conditional Gaussian distributions for the source machine and the target machine.

2. The method of claim 1 , wherein the multivariate Gaussian conditional distribution is expressed as y i |x i ˜N(f i (x i ), C y i (x i )), where y i represents a dependent sensor, x i represents an independent sensor, f i (x 1 ) represents a mean of the multivariate Gaussian conditional distribution of independent sensor x i , and C y i (x i ) represents a conditional covariance of dependent sensor y i given independent sensor x i .

3. The method of claim 2 , wherein the conditional covariance C y i (x i ) is a diagonal matrix.

4. The method of claim 3 , wherein the conditional covariance C y i (x i ) is a constant.

5. The method of claim 2 , wherein the transformation of independent sensor data samples from the source machine to the target machine is expressed by
x 12 =C x 2 1/2 C x 1 −1/2 ( x 1 −m x 1 )+ m x 2 ,
wherein x 1 represents independent sensor data for the source machine, m x 1 is a mean of the multivariate Gaussian distribution of an independent sensor of the source machine, m x 2 is a mean of the multivariate Gaussian distribution of an independent sensor of the target machine, C x 1 is a covariance of the multivariate Gaussian distribution of an independent sensor of the source machine, and C x 2 is a covariance of the multivariate Gaussian distribution of an independent sensor of the target machine.

6. The method of claim 5 , wherein the transformation of dependent sensor data samples from the source machine to the target machine is expressed by
y 12 =C y 2 1/2 ( x 12 ) C y 1 −1/2 ( x 1 )( y 1 −f 1 ( x 1 ))+ f 2 ( x 12 ), wherein
y 1 represents dependent sensor data for the source machine, y 2 represents dependent sensor data for the target machine, f 1 (x 1 ) is the mean of the multivariate conditional distribution of an independent sensor of the source machine, f 2 (x 12 ) is the mean of the multivariate conditional distribution of the transformed independent sensor for the target machine, C y 1 (x 1 ) is a covariance of the multivariate Gaussian distribution of a dependent sensor y 1 given independent sensor x 1 for the source machine, and C y 2 (x 12 ) is a covariance of the multivariate Gaussian distribution of a dependent sensor y 2 of the target machine given the transformed independent sensor x 12 .

7. The method of claim 6 , wherein the mean of the multivariate conditional distribution for a given machine is a regression function that maps independent sensor data for the given machine to dependent sensor data for the given machine, wherein the given machine is one of the source machine and the target machine.

8. The method of claim 1 , further comprising receiving sensor data samples for each of a source machine and a target machine, and partitioning sensor data samples for each machine into data from independent sensors, and data from dependent sensors that depend on data values of the independent sensors.

9. The method of claim 1 , further comprising:
learning the multivariate Gaussian distributions for the source machine and the target machine from the data samples from the independent sensors of the source machine and the target machine; and
learning the multivariate Gaussian conditional distributions for the source machine and the target machine from the data samples from the dependent sensors of said source machine and said target machine using the multivariate Gaussian distribution for the independent sensors.

10. A computer-implemented method for predicting failure modes in a machine, the method implemented by the computer comprising:
receiving sensor data samples for each of a source machine and a target machine, and partitioning sensor data samples for each machine into data from one or more independent sensors, and data from one or more dependent sensors whose sensor values depend on data values of the independent sensors, wherein said data samples are acquired under normal operating conditions for each machine;
transforming data samples for the independent sensors from the source machine to the target machine using a multivariate Gaussian distribution for the source machine and a multivariate Gaussian distribution for the target machine;
transforming data samples for the dependent sensors from the source machine to the target machine using the transformed independent sensor data samples and a conditional Gaussian distribution for the source machine and a conditional Gaussian distribution for the target machine;
acquiring data samples from the independent sensors of the source machine associated with a failure;
transforming said failure data samples for the independent sensors from the source machine to the target machine using the multivariate Gaussian distributions for the source machine and the target machine; and
transforming said failure data samples for the dependent sensors from the source machine to the target machine using the transformed independent sensor data samples and the conditional Gaussian distributions for the source machine and the target machine.

11. A non-transitory program storage device readable by a computer, tangibly embodying a program of instructions executed by the computer to perform the method steps for predicting failure modes in a machine, the method comprising:
learning a multivariate Gaussian distribution for each of a source machine and a target machine from data samples from one or more independent sensors of the source machine and the target machine, wherein said data samples are acquired under normal operating conditions for each machine;
learning a multivariate Gaussian conditional distribution for each of the source machine and the target machine from data samples from one or more dependent sensors of said source machine and said target machine using the multivariate

Assignees

• Siemens Ag

Inventors

• Yuan Chao
• Chakraborty Amit
• Hackstein Holger
• Weber Hans

Classifications

• G06F11/2257 Primary

  using expert systems · CPC title

• G05B23/0224

  Process history based detection method, e.g. whereby history implies the availability of large amounts of data · CPC title

• G06N99/005

  Physics · mapped topic

• G06N20/10 Primary

  using kernel methods, e.g. support vector machines [SVM] · CPC title

• G06N20/00

  Machine learning · CPC title