Abstract: An anomaly diagnosis system obtains a plurality of anomaly signals corresponding to a plurality of sensor signals of a physical system and segments one or more anomaly signals into a plurality of time segments. The system determines an anomaly score for each time segment based on anomaly values of the one or more anomaly signals during the time segment and identifies an anomaly time interval corresponding to at least one consecutive time segment within the plurality of time segments. The system clusters the plurality of anomaly signals within the anomaly time interval to identify an anomaly group of sensor signals associated with the anomaly time interval and determines an aggregate anomaly score for the anomaly group. The system generates a graphical user interface presenting a representation of the anomaly group of sensor signals and the aggregate anomaly scores and causes the graphical user interface to be displayed on a user device.

Abstract: A method comprises generating, from time series data, a plurality of tiles for each resolution of a plurality of resolutions, a first plurality of tiles associated with one resolution covering the same time period as a second plurality of tiles associated with another resolution, each tile having a common number of N values representing all measurements associated with a duration of time covered by the tile in the time series data; receiving a first user request specifying a first timestamp and a first resolution; determining that no tile is available based on the first timestamp and the first resolution; generating a first tile covering a first duration of time based on the first timestamp and the first resolution, a first number of measurements associated with the first duration of time being less than a second number of measurements associated with a second duration of time based on the first resolution; transmitting the first tile.

Abstract: A tile contains aggregated data at a certain resolution for the actual data present in the duration of time covered in that tile. Tiles are generated at every possible resolution suitable for a computer display and provide aggregate measures such as averages and variances. Tiles provide a summary of data such that from the highest level view down to the specific data time points collected, a user's attention may be drawn to the times when there is the most interesting pattern behavior to review and analyze.

Abstract: Model chaining provides users with enormous flexibility to define their systems in a way that best suits their needs to get the most benefit from artificial intelligence models. In model chaining, a model chain may be generated. Output of a model is used as the signal input to another model. In this way, lower-level models can be more sensitive as they find patterns using just a few signals, and higher-level model then looks for patterns in the patterns of the lower-level models. All of the signals are used while users are not being blinded by more subtle behaviors.

Abstract: A computer-implemented method of predicting an event horizon is disclosed. The method comprises maintaining condition data indicating a plurality of conditions occurring on one or more physical systems at a plurality of points in time. The method further comprises receiving an input feature vector representing a given condition occurring at a specific time during a specific period of time. The method also comprises generating, using a particular trained machine learning model of a plurality of trained machine learning models, a forecast value that indicates an amount of time from the specific time to an occurrence of a particular target condition on a particular physical system, the particular target condition being different from the given condition, each trained machine learning model corresponding to a distinct target condition. In addition, the method comprises causing, based on the forecast value, an action to be executed on the particular physical system.

Abstract: A method for generating forecast predictions that indicate an event horizon of an entity or remaining useful life of a consumable using machine learning techniques is provided. Using a server computer system, feature data comprising features vectors that represent a set of signal data over a range of time is stored. Condition data comprising conditions occurring on the entity at particular moments in time is stored. Label data that comprises a plurality of time values that each indicate a difference in time between one condition and another condition is stored. A training dataset is created by combining the feature data, the condition data, and the label data into a single dataset. The training dataset is partitioned by condition. A machine learning model is trained on each target condition training dataset. The trained machine learning models are used to generate forecast values that each indicate an amount of time to an occurrence of a target condition associated with an entity.

Abstract: A tile contains aggregated data at a certain resolution for the actual data present in the duration of time covered in that tile. Tiles are generated at every possible resolution suitable for a computer display and provide aggregate measures such as averages and variances. Tiles provide a summary of data such that from the highest level view down to the specific data time points collected, a user's attention may be drawn to the times when there is the most interesting pattern behavior to review and analyze.

Abstract: A method for generating forecast predictions that indicate an event horizon of an entity or remaining useful life of a consumable using machine learning techniques is provided. Using a server computer system, feature data comprising features vectors that represent a set of signal data over a range of time is stored. Condition data comprising conditions occurring on the entity at particular moments in time is stored. Label data that comprises a plurality of time values that each indicate a difference in time between one condition and another condition is stored. A training dataset is created by combining the feature data, the condition data, and the label data into a single dataset. The training dataset is partitioned by condition. A machine learning model is trained on each target condition training dataset. The trained machine learning models are used to generate forecast values that each indicate an amount of time to an occurrence of a target condition associated with an entity.

Abstract: In an embodiment, a data processing method comprises storing one or more generic machine operating definitions, wherein each of the generic machine operating definitions describes expected operational behavior of one or more types of machines during one or more operating states; analyzing operating data that describes past operation of a plurality of machines of a plurality of types; based at least in part on the operating data and the one or more generic machine operating definitions, generating and storing one or more machine operating models that describe expected operational behavior corresponding to a plurality of operating states of the plurality of machines; wherein the one or more machine operating models comprise a plurality of data patterns, wherein each of the data patterns is associated with a different set of one or more operating states of one or more machines; wherein the method is performed by one or more computing devices.

Abstract: A tile contains aggregated data at a certain resolution for the actual data present in the duration of time covered in that tile. Tiles are generated at every possible resolution suitable for a computer display and provide aggregate measures such as averages and variances. Tiles provide a summary of data such that from the highest level view down to the specific data time points collected, a user's attention may be drawn to the times when there is the most interesting pattern behavior to review and analyze.

Abstract: A tile contains aggregated data at a certain resolution for the actual data present in the duration of time covered in that tile. Tiles are generated at every possible resolution suitable for a computer display and provide aggregate measures such as averages and variances. Tiles provide a summary of data such that from the highest level view down to the specific data time points collected, a user's attention may be drawn to the times when there is the most interesting pattern behavior to review and analyze.

Abstract: A computer-implemented method of obtaining a label for graphically presented operating behavior is disclosed.

Abstract: A method for determining specific conditions occurring on industrial equipment based upon received signal data from sensors attached to the industrial equipment is provided. Using a server computer system, signal data is received and aggregated into feature vectors. Feature vectors represent a set of signal data over a particular range of time. The feature vectors are clustered into subsets of feature vectors based upon attributes the feature vectors. One or more sample episodes are received, where a sample episode includes sample feature vectors and specific classification labels assigned to the sample feature vectors. A signal data model is created that includes the associated feature vectors, clusters, and assigned classification labels.

Abstract: In an embodiment, a data processing method comprises storing one or more generic machine operating definitions, wherein each of the generic machine operating definitions describes expected operational behavior of one or more types of machines during one or more operating states; analyzing operating data that describes past operation of a plurality of machines of a plurality of types; based at least in part on the operating data and the one or more generic machine operating definitions, generating and storing one or more machine operating models that describe expected operational behavior corresponding to a plurality of operating states of the plurality of machines; wherein the one or more machine operating models comprise a plurality of data patterns, wherein each of the data patterns is associated with a different set of one or more operating states of one or more machines; wherein the method is performed by one or more computing devices.

Abstract: In an embodiment, a data processing method comprises storing one or more generic machine operating definitions, wherein each of the generic machine operating definitions describes expected operational behavior of one or more types of machines during one or more operating states; analyzing operating data that describes past operation of a plurality of machines of a plurality of types; based at least in part on the operating data and the one or more generic machine operating definitions, generating and storing one or more machine operating models that describe expected operational behavior corresponding to a plurality of operating states of the plurality of machines; wherein the one or more machine operating models comprise a plurality of data patterns, wherein each of the data patterns is associated with a different set of one or more operating states of one or more machines; wherein the method is performed by one or more computing devices.

Abstract: A computer-implemented method of obtaining a label for graphically presented operating behavior is disclosed.

Abstract: A method and apparatus for generating, causing display of, sending, or otherwise providing one or more interfaces for monitoring machines, classifying operating behavior of the machines, and/or predicting operational behavior of the machines. The interfaces may include a graphical user interface that that shows graphical elements changing in appearance over time according to a sequence of stored values that represent measurements. An interface manager may receive, via the graphical user interface, input comprising a label, and, based at least in part on the input, store the label in association with the displayed sequence or in association with a set or cluster of sequences that includes the displayed sequence.

Abstract: A method for determining specific conditions occurring on industrial equipment based upon received signal data from sensors attached to the industrial equipment is provided. Using a server computer system, signal data is received and aggregated into feature vectors. Feature vectors represent a set of signal data over a particular range of time. The feature vectors are clustered into subsets of feature vectors based upon attributes the feature vectors. One or more sample episodes are received, where a sample episode includes sample feature vectors and specific classification labels assigned to the sample feature vectors. A signal data model is created that includes the associated feature vectors, clusters, and assigned classification labels.

Abstract: Techniques are described for condition monitoring and prediction. According to embodiments described herein one or more computing devices receive a set of sensor data from a set of sensors. The sensor data identifies a condition associated with at least one mobile item moving through a particular geographic region and route that correspond to at least one cell in a transportation network representation. The one or more computing devices determine, from the sensor data, a correlation between the condition associated with the at least one mobile item and a set of one or more other conditions that are associated with the at least one cell in the route. The one or more computing device generate, based, at least in part, on the correlation, a prediction of a quality of service for the particular item that is associated with the route including the particular geographic region and corresponding to at least one cell in the transportation network representation.

Abstract: A method and apparatus for generating, causing display of, sending, or otherwise providing one or more interfaces for monitoring machines, classifying operating behavior of the machines, and/or predicting operational behavior of the machines. The interfaces may include a graphical user interface that that shows graphical elements changing in appearance over time according to a sequence of stored values that represent measurements. An interface manager may receive, via the graphical user interface, input comprising a label, and, based at least in part on the input, store the label in association with the displayed sequence or in association with a set or cluster of sequences that includes the displayed sequence.