Abstract: A method of detecting status changes, and a corresponding point-in-time, in monitored entities, includes receiving one or more elements of time-series data from one or more sensors, the elements of time-series data representing an operational state of the monitored entity, creating a predictive model from the time-series data in a datastore memory, applying a transduction classifier to the predictive model, the transduction classifier detecting a change from healthy to unhealthy in the time-series data, and the corresponding point-in-time when the change occurred, and providing an identification of the change in the time-series data and the corresponding point-in-time. In some embodiments the transduction classifier can be a maximum margin classifier having a support vector machine component and a temporal transductive component. A system and a non-transitory computer readable medium are also disclosed.

Abstract: According to some embodiments, a system and method are provided to create a template associated with an industrial problem. The method comprises receiving one or more kernels from a machine learning library. The one or more kernels are then aggregated, via a processor, into a template. The template is integrated into a user interface where is may be executed.

Abstract: According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a matching module comprising a processor, a dataset including two or more elements, wherein each of the two or more elements is one of a word and a document including one or more words; assigning at least one weight to each word in the dataset; calculating a weighted similarity score between two or more elements based on the assigned weight; determining whether the weighted similarity score is approved or rejected; and receiving the weighted similarity score at at least one of a user and another system. Numerous other aspects are provided.

Abstract: A system and method including, for each component of a system, defining filter flags that identify measurements that correspond to a particular operating condition of the respective component, the identified measurements being sensor measurements relevant to build a predictive model of expected output for each component of the system; defining input sensors for each of the components; defining at least one output sensor for each of the components; filtering data from the system based on the defined filter flags for each respective component; building, based on the defined input sensors for each respective component, a predictive model for the defined output sensor; determining a divergence between actual data values and expected values predicted by the model for each respective component; determining a component-specific anomaly score for each component of the system; and storing a record of the component-specific anomaly score for each component of the system.

Abstract: A method of detecting status changes, and a corresponding point-in-time, in monitored entities, includes receiving one or more elements of time-series data from one or more sensors, the elements of time-series data representing an operational state of the monitored entity, creating a predictive model from the time-series data in a datastore memory, applying a transduction classifier to the predictive model, the transduction classifier detecting a change from healthy to unhealthy in the time-series data, and the corresponding point-in-time when the change occurred, and providing an identification of the change in the time-series data and the corresponding point-in-time. In some embodiments the transduction classifier can be a maximum margin classifier having a support vector machine component and a temporal transductive component. A system and a non-transitory computer readable medium are also disclosed.

Abstract: According to some embodiments, a system and method are provided to create a template associated with an industrial problem. The method comprises receiving one or more kernels from a machine learning library. The one or more kernels are then aggregated, via a processor, into a template. The template is integrated into a user interface where is may be executed.

Abstract: A system, medium, and method including receiving input data relating to an employee, the input data including a plurality of sentences of descriptive language regarding the employee's performance; processing the input data to determine sentences of refined textual data; determining a category for each of the sentences of the refined textual data from a plurality of categories, each of the plurality of categories being different from each other and relating to a particular type of performance evaluation characteristic; generating, based on the refined textual data and the determined category for the sentences of the refined textual data, a plurality of summary sentences reflective of the input data; and generating a summarization of the employee's performance, the summarization including an ordered listing of the plurality of summary sentences.

Abstract: The present embodiments related to a machinery failure evaluation system and associated method. The system may receive time-series data associated with a piece of machinery. An anomaly associated with the piece of machinery may automatically be determined by comparing the time-series data with a model associated with the piece of machinery. Furthermore, it may be determined that the anomaly is not a known fault based on performing a lookup of known failure modes. In a case that the anomaly is not a known fault, an alert associated with an unknown failure mode may be transmitted.

Abstract: A system and method for text-mining to conduct diagnostics and prognostics using temporal multi-dimensional sensor observations is disclosed. A computer device stores historical time-series data for a plurality of systems. The computer device collects current time-series data from one or more sensors of a first system. The computer device compares the current time-series data to the historical time-series data to identify patterns in both the current time-series data and the historical time-series data. The computer device generates a failure likelihood prediction for the first system based on the identified patterns in the current time-series data and the historical time-series data.