Method and system for monitoring sensor data of rotating equipment
First Claim
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 streamrepresenting 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 bycalculating 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, orcreating 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, andcomparing 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, anddetecting a failure if the chosen historical model represents a failure state.
3 Assignments
0 Petitions
Accused Products
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.
32 Citations
20 Claims
-
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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
-
-
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 microcluster based on the new feature vector if all values for the correlation distance measure are above the range parameter, and detecting at least one of a change of orientation of the subspace of at least one of the microclusters, wherein the change of orientation exceeds a threshold, and creation of a new microcluster, 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, detecting a failure if the chosen historical model represents a failure state, and adjusting operation of the rotating equipment in response detecting the failure.
Specification