Abstract: A diagnostic/prognostics system for failure detection in an electrical insulation system is provided. The system includes at least two current transformers designed to detect high frequency component signals from the insulation system. The system also includes a data acquisition module coupled to the at least two current transformers, wherein the data acquisition module receives the high frequency component signals and analyzes the received high frequency component signals to identify one or more faulty components in the electrical insulation system.

Abstract: A fiber-optic dynamic sensing module comprises a support member, a beam extending from the support member, and a pre-strained fiber Bragg grating sensor and a strain-free fiber Bragg grating sensor mounted on the beam. The pre-strained and strain-free fiber Bragg grating sensors each comprise a Bragg grating inscribed in a fiber. The Bragg grating of the pre-strained fiber Bragg grating sensor is packaged more tightly along a longitudinal direction of the beam than the Bragg grating of the strain-free fiber Bragg grating sensor.

Abstract: A fiber-optic dynamic sensing module comprises a support member, a beam extending from the support member, and a pre-strained fiber Bragg grating sensor and a strain-free fiber Bragg grating sensor mounted on the beam. The pre-strained and strain-free fiber Bragg grating sensors each comprise a Bragg grating inscribed in a fiber. The Bragg grating of the pre-strained fiber Bragg grating sensor is packaged more tightly along a longitudinal direction of the beam than the Bragg grating of the strain-free fiber Bragg grating sensor.

Abstract: A system for predicting fall risk for a resident is provided. The system includes a fall risk prediction subsystem, configured to predict a fall risk likelihood for a resident based on data measurements collected from one or more sensors. One or more of the sensors comprise range control radar (RCR) sensors. The fall risk prediction subsystem further includes a fall analysis component configured to analyze the data measurements from one or more of the sensors and a fall risk assessment component configured to predict the fall risk likelihood for the resident based on the analysis.

Abstract: A system for predicting fall risk for a resident is provided. The system includes a fall risk prediction subsystem, configured to predict a fall risk likelihood for a resident based on data measurements collected from one or more sensors. One or more of the sensors comprise range control radar (RCR) sensors. The fall risk prediction subsystem further includes a fall analysis component configured to analyze the data measurements from one or more of the sensors and a fall risk assessment component configured to predict the fall risk likelihood for the resident based on the analysis.

Abstract: The reliability and remaining time before failure for electric motor systems and the like is determined with an ascertained statistical confidence based on continually monitored system parameters and a database comprised of historical failure mode/cause and repair data concerning the same motor and/or similar motor systems. A computational process is followed that relies upon hierarchical failure-tree structures, Weibull probability distributions and directed causal networks and which updates the probabilities and models used to forecast the remaining time before system failure based on the monitored parameters. Motor system operational parameter data is continually monitored at the site of the motor system and up-loaded via wireless or other communications network to a remote computer system where a database is maintained and reliability analysis is performed.

Abstract: The reliability and remaining time before failure for electric motor systems and the like is determined with an ascertained statistical confidence based on continually monitored system parameters and a database comprised of historical failure mode/cause and repair data concerning the same motor and/or similar motor systems. A computational process is followed that relies upon hierarchical failure-tree structures, Weibull probabillity distributions and directed causal networks and which updates the probabilities and models used to forecast the remaining time before system failure based on the monitored parameters. Motor system operational parameter data is continually monitored at the site of the motor system and up-loaded via wireless or other communications network to a remote computer system where a database is maintained and reliability analysis is performed.