A method of conditioning one or more actions related to an operator controllable device is disclosed. The method includes receiving one or more action-based instructions representing one or more operator controllable device actions associated with the operator controllable device, and receiving one or more action-oriented time representations representing one or more times associated with the action-based instructions. The method also includes deriving at least one conditioned action-based instruction for a time subsequent to the times represented by the received action-oriented time representations from: the received action-based instructions, and the received action-oriented time representations. The conditioned action-based instruction, when executed, causes the operator controllable device to take one or more conditioned actions. The method also includes producing at least one signal representing the conditioned action-based instruction, which when executed, causes the operator controllable device to take the conditioned action. Apparatuses, computer-readable storage media, and systems are also disclosed.

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.