Method and apparatus for optimizing diagnostics of rotating equipment
A method and an apparatus for optimizing diagnostics of rotating equipment is provided. The apparatus includes a device for providing status information about status of the rotating equipment over a series of time windows whereby status can be derived from sensor features of at least one available sensor taking measurements during a predefinable time period, a device for using deep learning which combines provided historic sensor information with sequence of events data indicating warnings and/or alerts of the rotating equipment, whereby status information is supplemented with via deep learning predicted probabilities whether a warning and/or an alert has occurred within a time window, device for providing an amount of textual diagnostic knowledge cases, device for extracting semantic information on text features from the textual diagnostic knowledge cases, and device for combining status information and semantic information into a unified representation enabling optimization of the diagnostics.
1. A method for optimizing diagnostics of rotating equipment, comprising:
providing status information about a status of the rotating equipment over a series of time windows wherein the status is derived from sensor measurements of at least one available sensor taking measurements during a predefinable time period,
identifying a given case of the rotating equipment and generating a sensor feature vector for the given case using deep learning which combines provided historic sensor information with sequence of events data indicating warnings and/or alerts of the rotating equipment, wherein deep learning uses a case-based reasoning learning method, wherein status information is supplemented with via deep learning predicted probabilities whether a warning and/or an alert has occurred within a time window of the series of time windows, and wherein said deep learning automatically identifies latent structure that makes two time windows of the series of time windows similar or dissimilar in order to predict the probabilities,
providing an amount of historic textual diagnostic knowledge cases,
extracting semantic information on text features from the historic textual diagnostic knowledge cases and from the given case, wherein a natural language training method is used for extracting said semantic information,
determining a text feature vector for the given case, and
combining the sensor feature vector for the given case and the text feature vector for the given case into a unified representation enabling optimization of the diagnostics, wherein the unified representation is one unified feature vector.
2. The method according to claim 1 , wherein such optimized diagnostics lead to adjust operation of rotation equipment and/or to maintain the rotation equipment.
3. The method according to claim 1 , wherein the result of this extraction is a set of text feature vectors, one said vector for each textual diagnostic knowledge case.
4. The method according to claim 3 , wherein the text feature vector for the given case is determined by classifying the given case against trained cases resulting in different clusters brought about case similarity computation wherein the vector contains as many cluster membership degrees as clusters exist.
5. The method according to according to claim 1 , wherein different weights are applied to different types of text features.
6. The method according to according to claim 1 , wherein different types of text features are affected parts and/or observed symptoms.
7. An apparatus for optimizing diagnostics of rotating equipment, comprising at least one hardware processor, wherein the apparatus is configured for:
providing status information about a status of the rotating equipment over a series of time windows wherein the status can be derived from sensor measurements of at least one available sensor taking measurements during a predefinable time period,
identifying a given case of the rotating equipment and generating a sensor feature vector for the given case using deep learning which combines provided historic sensor information with sequence of events data indicating warnings and/or alerts of the rotating equipment, wherein deep learning uses a case-based reasoning learning method, wherein status information is supplemented with via deep learning predicted probabilities whether a warning and/or an alert has occurred within a time window of the series of time windows, and wherein the deep learning automatically identifies latent structure that makes two time windows of the series of time windows similar or dissimilar in order to predict said probabilities,
providing an amount of historic textual diagnostic knowledge cases,
extracting semantic information on text features from the historic textual diagnostic knowledge cases and from the given case, wherein a natural language training method is used for extracting said semantic information,
determining a text feature vector for the given case, and
combining the sensor feature vector for the given case and the text feature vector for the given case into a unified representation enabling optimization of the diagnostics, wherein the unified representation is one unified feature vector.
8. The apparatus according to claim 7 , wherein such optimized diagnostics lead to adjust operation of rotation equipment and/or to maintain the rotation equipment.
9. The apparatus according to claim 3 , wherein the result of this extraction is a set of text feature vectors, one said vector for each textual diagnostic knowledge case.
10. The apparatus according to claim 9 , wherein the text feature vector for the given case can be determined by classifying the given case against trained cases resulting in different clusters brought about case similarity computation wherein the vector contains as many cluster membership degrees as clusters exist.
11. The apparatus according to claim 7 , wherein different weights can be applied to different types of text features.
12. The apparatus according to claim 7 , wherein different types of text features are affected parts and/or observed symptoms.
13. A computer program product, comprising a non-transitory computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to the method of claim 1 .