An apparatus for monitoring of a device including a moveable part, especially a rotating device, wherein the apparatus includes a control module which receives a measured vibration signal of the device provided by a sensor connected to the device, provides a spectrum of the measured vibration signal, pre-processes the spectrum to determine base frequencies and side frequencies, where the base frequencies are frequencies having peak powers corresponding to eigen frequencies of the device or faulty frequencies and the side frequencies correspond to other frequencies, where the control module additionally processes the base and side frequencies by applying separately a one-class classification on the base and side frequencies, combines the results of the one-class classifications to obtain a classification signal representing a confidence level, and outputs a decision support signal based on the classification signal, where the decision support signal indicates an error status of the monitored device.

The invention provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation over a time horizon.

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.

Embodiments of systems and methods for delivering a medicament using a pump are provided. The methods comprise inserting a first cannula into subcutaneous tissue. Medicament is delivered from a medicament pump through the first cannula according to a dosing protocol. The first cannula can removed after a period of time and a second cannula can be inserted. The method comprises modifying the dosing protocol based upon a cannula change indicator, the modifying comprising performing neural network calculations utilizing previously calculated error data, resulting in a modified dosing protocol. Medicament is delivered from the medicament pump through the second cannula according to the modified dosing protocol.

Systems and methods for improved human-machine dialog, include bidirectional translations notably through the translation of commands by the human into a form able to be manipulated by the machine, and conversely of results produced by the machine into a form intelligible to the human. Some developments describe notably the display of portions of intermediate reasoning followed by the machine (for example explanation of root causes).

A control apparatus providing a Dynamic Artificial Intelligence system, which employs data sets and software functions representing a plurality interactive software engine's, including Inference, Neural Net, State, and Proportional-Integral-Derivative (PID) Engines. These engines are implemented as a set of scheduled realtime monitors and callable functions with associated processes preformed within a system. Monitors dynamically estimates and determine the optimal control policy for the system and its sub-systems. Monitors utilize an iterative process of sub-steps “function calls’, until a convergence states exist. Functions and subfunctions dynamically estimate the desired value for operation at a respective state of the environment over a series of predicted environmental states; using a complex return of data sets to determine bounds to improve the estimated currently desired value; and producing updated estimates of optimal control policies. DAI further interacts in realtime with external events to modify control policies.

A technique is provided for control of a nonlinear dynamical system to an arbitrary trajectory. The technique does not require any knowledge of the dynamical system, and thus is completely model-free. When applied to a chaotic system, it is capable of stabilizing unstable periodic orbits (UPOs) and unstable steady states (USSs), controlling orbits that require non-vanishing control signal, synchronization to other chaotic systems, and so on. It is based on a type of recurrent neural network (RNN) known as a reservoir computer (RC), which, as shown, is capable of directly learning how to control an unknown system. Precise control to a desired trajectory is obtained by iteratively adding layers to the controller, forming a deep recurrent neural network.

A hierarchical model predictive control (HMPC) method based on fuzzy neural network for wastewater treatment process (WWTP) is designed to realize hierarchical control of dissolved oxygen (DO) concentration and nitrate nitrogen concentration. In view of the difference of time scales in WWTP, it is difficult to accurately control the concentration of DO and nitrate nitrogen. The disclosure establishes a HMPC structure according to different time scales. Then, the concentration of DO and nitrate nitrogen is controlled with different frequencies. It not only conforms to the operation characteristics of WWTP, but also solves the problem of poor operation performance of multivariable model predictive control. The experimental results show that the HMPC method can achieve accurate on-line control of DO concentration and nitrate nitrogen concentration with different time scales.

Arrangements for dynamic system control evaluation and issue identification and mitigation are provided. In some examples, data may be received from a plurality of sources. The data may be received in batches at predetermined intervals or time periods, and/or as streaming data. In some examples, a first system control may be identified and a first system control value may be determined for the first system control. A plurality of threshold ranges associated with the first system control may be identified and the first system control value may be compared to the plurality of threshold ranges. Based on the comparing, the first system control value may be mapped to an objective score on a cyber health scale. The objective score may then be evaluated to determine whether an issue is occurring or is likely to occur. If so, one or more mitigation actions may be identified and implemented.

A method for assisting maneuver planning for a transportation vehicle driving by automation or for a robot; wherein a state space of an environment of the transportation vehicle or the robot is discretely described by a Markov decision process; wherein optimal action values for discretized actions are determined by dynamic programming, based on discrete states in the state space; wherein a mapping with states in the state space as input values, and with action values for actions in the state space as output values, is learned by a reinforcement learning method; wherein a reinforcement learning agent is initialized based on the optimal action values determined by the dynamic programming; and wherein the learned mapping is provided for maneuver planning. Also disclosed is a device for assisting maneuver planning for a transportation vehicle driving by automation or for a robot.