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 system and methods to identify which signals are significant to an assessment of a complex machine system state in the presence of non-linearities and disjoint groupings of condition types. The system enables sub-grouping of signals corresponding to system sub-components or regions. Explanations of signal significance are derived to assist in causal analysis and operational feedback to the system is prescribed and implemented for the given condition and causality.

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 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 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: A system and methods to identify which signals are significant to an assessment of a complex machine system state in the presence of non-linearities and disjoint groupings of condition types. The system enables sub-grouping of signals corresponding to system sub-components or regions. Explanations of signal significance are derived to assist in causal analysis and operational feedback to the system is prescribed and implemented for the given condition and causality.

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.