Abstract: A system and related method for building and deploying one or more inference models for use in remote condition monitoring of a first fleet of a first asset. The system includes model configuration data for subsequent use by a model builder application to construct one or more desired inference models for the first asset. The model configuration data is customized to the first asset and the desired one or more inference models, and is provided in a format which is easily readable and editable by a user of the system. The model configuration data is separate from the underlying processing algorithms which are employed by the model builder application in the constructing of the one or more desired inference models during a learning mode of operation of the system.

Abstract: A method of identifying anomalies in a monitored system includes acquiring input data from a plurality of sensors in the monitored system. Preprocessing the acquired data to prepare it for modeling leaves a first data subset that feeds into a normal Gaussian mixture model built using normal operating conditions of the monitored system. Removing data flagged as anomalous by the normal Gaussian mixture model leaves a second data subset that is compared to at least one threshold. If the comparison indicates that the second data subset contains anomalies, then the second data subset feeds into at least one of a set of asset performance Gaussian mixture models. Identifying which data contribute to an abnormality in the monitored system leaves a third data subset. Post-processing the third data subset may extract anomalies in the monitored system.

Abstract: A system and related method for building and deploying one or more inference models for use in remote condition monitoring of a first fleet of a first asset. The system includes model configuration data for subsequent use by a model builder application to construct one or more desired inference models for the first asset. The model configuration data is customized to the first asset and the desired one or more inference models, and is provided in a format which is easily readable and editable by a user of the system. The model configuration data is separate from the underlying processing algorithms which are employed by the model builder application in the constructing of the one or more desired inference models during a learning mode of operation of the system.