METHOD AND SYSTEM FOR MONITORING SENSOR DATA OF ROTATING EQUIPMENT

• US 20170365155A1
• Filed: 06/15/2017
• Published: 12/21/2017
• Est. Priority Date: 06/17/2016
• Status: Active Grant

• Current Assignee: Siemens AG
• Original Assignee: Siemens AG
• Inventors: Borutta, Felix, Hubauer, Thomas, Krger, Peer
• Granted Patent: US 10,339,784 B2
• Time in Patent Office: Days
• CPC Class Codes: G05B 2219/32201 Build statistical model of past normal proces, compare with actual process
• CPC Class Codes: G05B 23/0221 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 Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G06F 18/2135 based on approximation criteria, e.g. principal component analysis
• CPC Class Codes: G06F 18/24137 Distances to cluster centroïds
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G07C 3/02 Registering or indicating working or idle time only
• CPC Class Codes: G08B 21/187 Machine fault alarms
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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.

Claims

1-11. -11. (canceled)

12. A method for monitoring sensor data of rotating equipment, with the following steps:

processing a sensor data stream consisting of an ordered sequence of feature vectors, each feature vector representing measurements of sensors of the rotating equipment at a certain point in time,

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 microcluster based on the new feature vector if all values for the correlation distance measure are above the range parameter.

13. The method according to claim 12, 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.

14. The method according to claim 12, 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.

15. The method according to claim 12, 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.

16. The method according to claim claim 15,

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.

17. The method according to claim 15,

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.

18. The method according to claim 17,

wherein the incremental Principal Component Analysis uses an exponential fading function assigning each feature vector a weight which decreases exponentially with time.

19. The method according to claim 15, with the additional step of

periodically scanning the set of microclusters and deleting microclusters whose timestamp is older than a threshold value.

20. The method according to claim 12, with an additional offline phase which includes

creating 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.

21. The method according to claim 20, 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.

22. The method according to claim 21,

with the additional step of merging related macroclusters from different partitions.

23. The method according to claim 20,

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.

24. The method according to claim 20,

wherein the comparison measure computes a composition of an approximate linear dependency and an affine distance between two microclusters.

25. The method according to claim 20, with the additional step of

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.

26. The method according to claim 25, 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.

27. The method according to claim 25, with the additional step of

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.

28. The method according to claim 12, wherein

an ageing mechanism is used to forget an influence of old parts of the sensor data stream to the microclusters.

29. The method according to claim 12, with the additional step of

adjusting operation of the rotating equipment and/or maintenance of the rotating equipment.

30. 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 12.

31. A 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 12.

32. A computer program,

which is being executed by one or more processors of a computer system and performs the method according to claim 12.