Method and system for monitoring sensor data of rotating equipment

Patent metadata

Field	Value
Publication number	US-10339784-B2
Application number	US-201715623409-A
Country	US
Kind code	B2
Filing date	Jun 15, 2017
Priority date	Jun 17, 2016
Publication date	Jul 2, 2019
Grant date	Jul 2, 2019

Abstract

A sensor data stream is provided consisting of feature vectors acquired by sensors of rotating equipment, similar feature vectors are aggregated in microclusters. For newly arriving feature vectors, a correlation distance measure between the new feature vector and each microcluster is calculated. If there is no microcluster in range, then a new microcluster is created. Otherwise, the feature vector is assigned to the best fitting microcluster, and the necessary statistical information is incorporated into the aggregation contained in the microcluster. In other words, similar feature vectors are aggregated in the same microclusters. The microclusters thus provide a generic summary structure that captures the necessary statistical information of the incorporated feature vectors. At the same time, the loss of accuracy is quite small. Clustering the sensor data stream with microclusters has the benefit that the computational complexity can be reduced significantly.

First claim

The invention claimed is:

1. A method for detecting a failure in rotating equipment based on monitoring sensor data of the rotating equipment, wherein the method comprises:
collecting, during an online phase, by a plurality of sensors of the rotating equipment, a sensor data stream, wherein the data stream consists of an ordered sequence of feature vectors, each feature vector representing measurements of at least one sensor of the plurality of sensors of the rotating equipment at a certain point in time,
providing the sensor data stream to a processor,
processing, by the processor, the sensor data stream
representing the sensor data stream with a set of microclusters, each microcluster defining a subspace,
for each new feature vector of the sensor data stream, updating the set of microclusters by
calculating a correlation distance measure between the new feature vector and each microcluster,
assigning the new feature vector to a microcluster with a smallest value for the correlation distance measure if the value is below a range parameter and updating the microcluster based on the new feature vector, or
creating a new microcluster based on the new feature vector if all values for the correlation distance measure are above the range parameter,

creating, during an offline phase, a macrocluster model containing macroclusters based on the microclusters by calculating a comparison measure between each pair of microclusters and grouping microclusters in a macrocluster if their value of the comparison measure is below a threshold, and
comparing the macrocluster model with historical models by calculating a similarity measure, with each historical model representing either a standard operation or a failure state,
choosing the historical model with the highest value of the similarity measure, and
detecting a failure if the chosen historical model represents a failure state.

2. The method according to claim 1 , with the additional step of
detecting the possibility of a failure in real time
if a change of orientation of the subspace of at least one of the microclusters exceeds a threshold, or
if at least one new microcluster has been created.

3. The method according to claim 1 , with the additional step of
detecting the possibility of a failure in real time by continuously comparing newly created microclusters with a database containing microclusters representing known failure states.

4. The method according to claim 1 , with each microcluster comprising
a mean vector of feature vectors contained in the microcluster,
a timestamp of the last incoming feature vector assigned to the microcluster,
a buffer containing incoming feature vectors, if the microcluster has not been initialized, and
an eigenvector matrix containing eigenvectors, and eigenvalues for the eigenvectors, if the microcluster has already been initialized.

5. The method according to claim claim 4 ,
wherein for microclusters that have not been initialized, instead of calculating the correlation distance measure calculating an Euclidean distance between the new feature vector and the mean vector of the microcluster.

6. The method according to claim 4 ,
wherein updating the microcluster based on the new feature vector comprises the alternatives of
if the microcluster has not been initialized, inserting the new feature vector into its buffer, recalculating its mean vector and updating its timestamp, and if the buffer is now filled, initializing the microcluster by performing an initial Principal Component Analysis to calculate its eigenvectors and eigenvalues, or
if the microcluster has already been initialized, performing an incremental Principal Component Analysis to recalculate its eigenvectors and eigenvalues, recalculating its mean vector and updating its timestamp.

7. The method according to claim 6 ,
wherein the incremental Principal Component Analysis uses an exponential fading function assigning each feature vector a weight which decreases exponentially with time.

8. The method according to claim 4 , with the additional step of
periodically scanning the set of microclusters and deleting microclusters whose timestamp is older than a threshold value.

9. The method according to claim 1 , wherein the macrocluster model is created by
partitioning the set of microclusters,
computing the macroclusters within each partition, and
building a hierarchy of the macroclusters.

10. The method according to claim 9 ,
with the additional step of merging related macroclusters from different partitions.

11. The method according to claim 1 ,
wherein the comparison measure is calculated by comparing an orientation of the subspaces of the microclusters and grouping microclusters if the difference of the orientation of their subspaces is below a threshold.

12. The method according to claim 1 ,
wherein the comparison measure computes a composition of an approximate linear dependency and an affine distance between two microclusters.

13. The method according to claim 9 , with the additional step of
ranking the historical models according to the value of the similarity measure, and
outputting a ranked list of the historical models with the highest values of the similarity measure.

14. The method according to claim 1 , wherein
an ageing mechanism is used to forget an influence of old parts of the sensor data stream to the microclusters.

15. The method according to claim 1 , with the additional step of
adjusting operation of the rotating equipment and/or maintenance of the rotating equipment.

16. A system for monitoring sensor data of rotating equipment, the system comprising one or more processors which are programmed to perform the method according to claim 1 .

17. A computer program product comprising a non-transitory computer-readable storage media having stored thereon:
instructions executable by one or more processors of a computer system, wherein execution of the instructions causes the computer system to perform the method according to claim 1 .

18. The method of claim 1 , wherein the method is implemented by a computer readable program code having program commands, wherein the computer readable program code having program commands is stored on a computer program product comprising a non-transitory computer readable hardware storage device, and
wherein the computer readable program code is configured to be executed by one or more processors of a computer system.

19. A method for adjusting operation of rotating equipment in response to monitoring sensor data of the rotating equipment, wherein the method comprises
collecting, during an online phase, by a plurality of sensors of the rotating equipment, a sensor data stream, wherein the data stream consists of an ordered sequence of feature vectors, each feature vector representing measurements of at least one sensor of the plurality of sensors of the rotating equipment at a certain point in time,
processing the sensor data stream by a processor,
representing the sensor data stream with a set of microclusters, each microcluster defining a subspace,
for each new feature vector of the sensor data stream, updating the set of microclusters by
calculating a correlation distance measure between the new feature vector and each microcluster, and
assigning the new feature vector to a microcluster with a smallest value for the correlation distance measure if the value is below a range parameter and updating the microcluster based on the new feature vector, or
creating a new microcluste

Assignees

• Siemens Ag

Inventors

• Borutta Felix
• Hubauer Thomas
• Kröger Peer

Classifications

• G06N20/00 Primary

  Machine learning · CPC title

• G08B21/187 Primary

  Machine fault alarms · CPC title

• G06F18/24137

  Distances to cluster centroïds · CPC title

• G06F18/2135

  based on approximation criteria, e.g. principal component analysis · CPC title

• G05B23/0221

  Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods · CPC title