Abstract: Raw data is received from an industrial machine. The industrial machine includes one or more sensors that obtain the data, and the sensors transmit the raw data to a central processing center. The raw data is received at the central processing center and an unsupervised kernel-based algorithm is recursively applied to the raw data. The application of the unsupervised kernel-based algorithm is effective to learn characteristics of the raw data and to determine from the raw data a class of acceptable data. The class of acceptable data is data having a degree of confidence above a predetermined level that the data was obtained during a healthy operation of the machine. The acceptable data is successively determined and refined upon each application of the unsupervised kernel-based algorithm. The unsupervised kernel-based algorithm is executed until a condition is met.

Abstract: A system and method for associating equipment sensor tags with tags associated with analytic programs including embodiments that receive a list of equipment tag names and corresponding descriptions as well as a list of analytic tag names and descriptions. The equipment tag descriptions are normalized to create normalized equipment tag descriptions, and the analytic tag descriptions are normalized to create normalized analytic tag descriptions. A first matrix of vectors that associates content in a dictionary with aspects of the equipment tag names is created using corresponding normalized tag descriptions. A second matrix of vectors that associates the content in a dictionary with aspects of the normalized analytic tag description is created.

Abstract: Raw data is received from an industrial machine. The industrial machine includes one or more sensors that obtain the data, and the sensors transmit the raw data to a central processing center. The raw data is received at the central processing center and an unsupervised kernel-based algorithm is recursively applied to the raw data. The application of the unsupervised kernel-based algorithm is effective to learn characteristics of the raw data and to determine from the raw data a class of acceptable data. The class of acceptable data is data having a degree of confidence above a predetermined level that the data was obtained during a healthy operation of the machine. The acceptable data is successively determined and refined upon each application of the unsupervised kernel-based algorithm. The unsupervised kernel-based algorithm is executed until a condition is met.

Abstract: A system and method for associating equipment sensor tags with tags associated with analytic programs including embodiments that receive a list of equipment tag names and corresponding descriptions as well as a list of analytic tag names and descriptions. The equipment tag descriptions are normalized to create normalized equipment tag descriptions, and the analytic tag descriptions are normalized to create normalized analytic tag descriptions. A first matrix of vectors that associates content in a dictionary with aspects of the equipment tag names is created using corresponding normalized tag descriptions. A second matrix of vectors that associates the content in a dictionary with aspects of the normalized analytic tag description is created.

Abstract: Streaming data at a sensor is sensed and received. The streaming data includes a plurality of observations. For a current observation in the plurality of observations, a health of the current observation is determined. Based upon the health of the current observation, a penalty is determined. A Sensor Health Index (SHI) for the current observation is obtained by aggregating the penalty with at least one SHI of one or more previous observations from the plurality of observations. An alarm is selectively generated based upon the SHI of the current observation.

Abstract: Streaming data at a sensor is sensed and received. The streaming data includes a plurality of observations. For a current observation in the plurality of observations, a health of the current observation is determined. Based upon the health of the current observation, a penalty is determined. A Sensor Health Index (SHI) for the current observation is obtained by aggregating the penalty with at least one SHI of one or more previous observations from the plurality of observations. An alarm is selectively generated based upon the SHI of the current observation.