A predictive maintenance system for an irrigation system includes one or more sensors configured to generate a signal indicative of abnormal operation within the irrigation system, the sensors electrically coupled to a drive system, a processor, and a memory. The memory includes instructions stored thereon, which when executed by the processor cause the predictive maintenance system to receive the generated signal, determine abnormal operation of the drive system based on the generated signal, and predict, by a machine learning model, a maintenance requirement of the drive system based on the determined abnormal operation.

A state estimation device calculates a state transition table indicating a state transition assumed in an object every time a connection pattern between partial waveforms is changed, selects a connection pattern from the state transition table on the basis of entropy that is a statistical index of the state transition of the object, and estimates a state of the object at each time and a state transition of the object on the basis of the selected connection pattern.

A control system for identifying and responding to effective system changes in a monitored system includes data collection, change identification, classifier construction, effective system change and parameter adjustment modules. The data collection module tracks parameters of the monitored system. The change identification module, based on the parameters, identifies: during training, performance changes and first system changes; and post training, a set of performance changes and second system changes. The classifier construction module, based on the performance changes and the first system changes construct a performance-system change classifier module. The performance-system change classifier module, based on the set of performance changes, determines possible system changes and respective probability values. The effective system change module, based on the second and possible system changes, determines effective system changes and respective probability values. The parameter adjustment module controls an actuator of the monitored system based on the effective system changes and the probability values.

In an information processing device according to the present invention, a statistics estimation unit estimates a value of a state quantity by using a statistical model constructed based on values of past state quantities of a target device. A physical estimation unit estimates a value of a state quantity by using a physical model constructed based on design data of the target device. A specification unit specifies a value to be used to manage the target device from the value estimated by the statistics estimation unit and the value estimated by the physical estimation unit based on deterioration of the target device with time.

A chemical system for an operation exhibiting steady-state gain inversion is provided herein and includes a reactor configured to receive a feed stream and produce an outlet stream to form a process and a control device configured to control a process. The control device receives inputs indicative of an operational parameter and output variables and, in response to the inputs and output variables, provides a steady-state manipulated input configured to control or optimize the process. The control device includes an input disturbance model, a state estimator, a non-linear steady-state target calculator, and a regulator configured to provide a signal for adjustment of one or more inputs based on the steady-state manipulated input and associated output variables.

A control system for identifying and responding to effective system changes in a monitored system includes data collection, change identification, classifier construction, effective system change and parameter adjustment modules. The data collection module tracks parameters of the monitored system. The change identification module, based on the parameters, identifies: during training, performance changes and first system changes; and post training, a set of performance changes and second system changes. The classifier construction module, based on the performance changes and the first system changes construct a performance-system change classifier module. The performance-system change classifier module, based on the set of performance changes, determines possible system changes and respective probability values. The effective system change module, based on the second and possible system changes, determines effective system changes and respective probability values. The parameter adjustment module controls an actuator of the monitored system based on the effective system changes and the probability values.

Methods and apparatus to implement predictive analytics for continuous processes are disclosed. An example apparatus includes a virtual batch unit controller to implement a sampling batch on a virtual batch unit. The sampling batch corresponds to a discrete period of time of a continuous control system process. The virtual batch unit includes input and output parameters corresponding to parameters associated with the continuous control system process. The example apparatus further includes a sampling batch analyzer to generate predictive analytic information indicative of a predicted quality of an output of the continuous control system process at an end of the discrete period of time based on an analysis of the sampling batch relative to an analytical model.

A system and method for monitoring underground drilling in which vibration is monitored by creating a model of the drill string using finite element techniques or finite difference techniques and (i) predicting vibration by inputting real time values of operating parameters into the model, and adjusting the model to agree with measured vibration data, (ii) predicting the weight on bit and drill string and mud motor speeds at which resonance will occur, and when stick-slip will occur, to avoid operating regimes that will result in high vibration, (iii) determining vibration and torque levels along the length of the drill string based on the measured vibration and torque at one or more locations, (iv) determining the remaining life of critical components of the drill string based on the history of the vibration, and (v) determining the optimum drilling parameters that will avoid excessive vibration of the drill string.

Methods and apparatus to implement predictive analytics for continuous processes are disclosed. An example apparatus includes a virtual batch unit controller to implement a sampling batch on a virtual batch unit. The sampling batch corresponds to a discrete period of time of a continuous control system process. The virtual batch unit includes input and output parameters corresponding to parameters associated with the continuous control system process. The example apparatus further includes a sampling batch analyzer to generate predictive analytic information indicative of a predicted quality of an output of the continuous control system process at an end of the discrete period of time based on an analysis of the sampling batch relative to an analytical model.

Provided is an information processing device, etc., that provides information which is the basis for quick detection of abnormalities that occur in a device. An information processing device calculates a degree of suitability between observation information and prediction information, the observation information observed for a system suffering an effect from an certain device, the prediction information predicted in accordance with a model for a state of the system; and calculates a difference between manipulation amount to the certain device and predictive manipulation amount predicted for the manipulation amount based on the model, the difference being a difference in case that the degree satisfies a predetermined calculation condition.