Abstract: A method for identifying a cause of a machine operating anomaly including creating a reduced order model (ROMs) for a digital twin model of a selected machine type and feeding current data from a deployed machine into the ROM. The method can include comparing a current output from the selected ROM with a measured output from the current data and determining that an operating anomaly exists when the difference between the current output and the measured output exceeds a selected anomaly threshold.

Abstract: Systems and methods for lubricant dilution detection are disclosed. A method for detecting lubricant dilution for a lubrication system includes detecting a low idle condition. The method includes receiving sensed values indicative of lubricant pressure and lubricant temperature during the low idle condition. The method also includes determining a lubricant pressure threshold based on the sensed value indicative of lubricant temperature. The method further includes determining lubricant dilution based on the sensed value indicative of lubricant pressure and the determined lubricant pressure threshold during the low idle condition. In accordance with a determination that there is lubricant dilution, the method includes outputting an indication of the lubricant dilution.

Abstract: A method for identifying a cause of a machine operating anomaly including creating a reduced order model (ROMs) for a digital twin model of a selected machine type and feeding current data from a deployed machine into the ROM. The method can include comparing a current output from the selected ROM with a measured output from the current data and determining that an operating anomaly exists when the difference between the current output and the measured output exceeds a selected anomaly threshold.

Abstract: A computer implemented method for predicting equipment failure by monitoring equipment data, the method comprising: generating a first set of predictions by processing equipment data via a plurality of first models of data analysis and machine learning techniques; generating a second set of predictions by processing equipment data via a plurality of second models of data analysis and machine learning techniques; generating, using machine learning techniques, a consensus decision by comparing the first set of predictions and the second set of predictions; estimating, using machine learning techniques, a level of confidence for the consensus decision; and selectively disclosing the consensus decision qualifying a confidence threshold.