Abstract: A system is provided comprising a memory configured to store instructions and a processor configured to execute the instructions. The processor is configured to execute the instructions to receive sensor data comprising sensed operations for a machinery, the sensed operations sensed via one or more sensors disposed in the machinery, and to derive a first model matrix based on the sensor data. The processor is further configured to derive a covariance regression model based on the first model matrix, wherein the covariance regression model is configured to be executed to derive a predictive event based on operational machinery data as input.

Abstract: Embodiments of the disclosure can relate to predicting valve performance in power plants. In one embodiment, a method for predicting valve performance in power plants can include receiving at least one signal from a valve associated with a power plant. The method may further include receiving operational data from one or more power plants. The method may further include determining a confidence value associated with operation of the valve, based at least in part on the at least one signal from the valve and the operational data from one or more power plants. The method can further include comparing the confidence value to a threshold level, and comparing a time during which the confidence value persists to a threshold duration. When the confidence value exceeds the threshold level, and when the time exceeds the threshold duration, the method may include generating an alert for a probability of misoperation of the valve.

Abstract: A system is provided comprising a memory configured to store instructions and a processor configured to execute the instructions. The processor is configured to execute the instructions to receive sensor data comprising sensed operations for a machinery, the sensed operations sensed via one or more sensors disposed in the machinery, and to derive a first model matrix based on the sensor data. The processor is further configured to derive a covariance regression model based on the first model matrix, wherein the covariance regression model is configured to be executed to derive a predictive event based on operational machinery data as input.