A system for predicting equipment failure events and optimizing manufacturing operations, the system comprising: an interface for receiving sensor data respectively associated with one or more assets ; a processor in communication with the interface ; and non-transitory computer readable media in communication with the processor that stores instruction code , which when executed by the processor , causes the processor to: input received sensor data into a first model to train the first model as a stochastic degradation model for determining a predicted degradation state of a particular asset ; input received sensor data associated with the particular asset into the trained first model to determine a predicted degradation state of the particular asset ; input the determined predicted degradation state of the particular asset into a second model trained to provide a prediction of a future root cause for a failure of the particular asset in a form of a covariate , wherein the first model is arranged to determine predicted degradation states of assets that are compensated with predicted failure events based on covariates provided by the second model ; input the determined compensated predicted degradation state of the particular asset into a reinforcement learning engine configured to generate a maintenance schedule ; and communicate the maintenance schedule to an entity associated with the particular asset or the particular asset. an interface for receiving sensor data respectively associated with one or more assets ; a processor in communication with the interface ; and non-transitory computer readable media in communication with the processor that stores instruction code , which when executed by the processor , causes the processor to: input received sensor data into a first model to train the first model as a stochastic degradation model for determining a predicted degradation state of a particular asset ; input received sensor data associated with the particular asset into the trained first model to determine a predicted degradation state of the particular asset ; input the determined predicted degradation state of the particular asset into a second model trained to provide a prediction of a future root cause for a failure of the particular asset in a form of a covariate , wherein the first model is arranged to determine predicted degradation states of assets that are compensated with predicted failure events based on covariates provided by the second model ; input the determined compensated predicted degradation state of the particular asset into a reinforcement learning engine configured to generate a maintenance schedule ; and communicate the maintenance schedule to an entity associated with the particular asset or the particular asset. input received sensor data into a first model to train the first model as a stochastic degradation model for determining a predicted degradation state of a particular asset ; input received sensor data associated with the particular asset into the trained first model to determine a predicted degradation state of the particular asset ; input the determined predicted degradation state of the particular asset into a second model trained to provide a prediction of a future root cause for a failure of the particular asset in a form of a covariate , wherein the first model is arranged to determine predicted degradation states of assets that are compensated with predicted failure events based on covariates provided by the second model ; input the determined compensated predicted degradation state of the particular asset into a reinforcement learning engine configured to generate a maintenance schedule ; and communicate the maintenance schedule to an entity associated with the particular asset or the particular asset. <EOS>