Abstract: A method for predicting conditions associated with a coal stock pile is described. The method includes collecting aerial data for a site including one or more coal stock piles. Using the aerial data, the method includes performing localization of the site to identify boundaries of the coal stock piles and extracting multi-spectral features. The method also includes obtaining additional data associated with the coal stock piles from at least one data source and merging the aerial data with the additional data. Using the merged data and the extracted multi-spectral features, the method includes analyzing a status of the coal stock piles by a prediction module to predict at least one of an impending combustion event or a severe condition associated with the coal stock piles. In response to the predicted at least one impending combustion event or severe condition, the method includes implementing a response.

Abstract: An AI-based asset maintenance system accesses a variety of data sources related to an entity to analyze data regarding one or more damage mechanisms corresponding to the entity thereby identifying and implementing corrective actions that mitigate the effects of the damage mechanisms within the entity. The accessed data is stored using a parameterized data model that represents the entity. A trained parameter model identifies the most significant operating parameters for a given component of the entity for the damage mechanism affecting the component. A projection model is used to perform ‘what-if’ analysis of the most significant operating parameters for determining the instances of minimum and maximum degradation due to the damage mechanism. Corrective actions for mitigating the degradation due to the damage mechanism can be determined based on analysis of the operating parameters and other attributes corresponding to the best and worst case degradation scenarios.

Abstract: A method for predicting conditions associated with a coal stock pile is described. The method includes collecting aerial data for a site including one or more coal stock piles. Using the aerial data, the method includes performing localization of the site to identify boundaries of the coal stock piles and extracting multi-spectral features. The method also includes obtaining additional data associated with the coal stock piles from at least one data source and merging the aerial data with the additional data. Using the merged data and the extracted multi-spectral features, the method includes analyzing a status of the coal stock piles by a prediction module to predict at least one of an impending combustion event or a severe condition associated with the coal stock piles. In response to the predicted at least one impending combustion event or severe condition, the method includes implementing a response.

Abstract: An AI-based asset maintenance system accesses a variety of data sources related to an entity to analyze data regarding one or more damage mechanisms corresponding to the entity thereby identifying and implementing corrective actions that mitigate the effects of the damage mechanisms within the entity. The accessed data is stored using a parameterized data model that represents the entity. A trained parameter model identifies the most significant operating parameters for a given component of the entity for the damage mechanism affecting the component. A projection model is used to perform ‘what-if’ analysis of the most significant operating parameters for determining the instances of minimum and maximum degradation due to the damage mechanism. Corrective actions for mitigating the degradation due to the damage mechanism can be determined based on analysis of the operating parameters and other attributes corresponding to the best and worst case degradation scenarios.