Abstract: A system and a method for predicting failure in a power system in real-time. The method comprises obtaining, by a processing unit, state estimation data corresponding to electrical quantities of the power system received from one or more sources in real-time, extracting a feature vector from the received state estimation data based on contingency analysis information using a trained machine learning model, wherein the feature vector corresponds to one or more parameters pertaining to the power system in real-time, determining a security index for the received state estimation data based on the extracted feature vector using the trained machine learning model and, predicting a failure of the power system based on the determined security index.

Abstract: Hardware based unsupervised based machine-learning (ML) approach to identify a security threat to the processor (e.g., caused by probing of a power supply rail). An apparatus is provided which includes an on-die power supply droop detector as a feature extractor. The droop detector detects a droop in the power supply caused by a probe physically coupling to the power supply rail. The droop detector in combination with machine-learning logic detects change in power supply rail impedance profile due to a probe coupled to the power supply rail. A deep-neural network (DNN) is provided for feature classification that classifies a security threat from normal operation and from operations caused by aging of devices in the processor. The DNN is trained in a training phase or production phase of the processor. An aging sensor is used to distinguish classification of aged data vs. normal data and data from security attack.

Abstract: Control loop latency can be accounted for in predicting positions of micro-objects being moved by using a hybrid model that includes both at least one physics-based model and machine-learning models. The models are combined using gradient boosting, with a model created during at least one of the stages being fitted based on residuals calculated during a previous stage based on comparison to training data. The loss function for each stage is selected based on the model being created. The hybrid model is evaluated with data extrapolated and interpolated from the training data to prevent overfitting and ensure the hybrid model has sufficient predictive ability. By including both physics-based and machine-learning models, the hybrid model can account for both deterministic and stochastic components involved in the movement of the micro-objects, thus increasing the accuracy and throughput of the micro-assembly.

Abstract: Possible input value combinations of a prediction of an engineered system are iterated over, comprising, for a possible input value combination: selecting an action to perform on the engineered system for the possible input value combination, comprising: performing a plurality of predictions of the engineered system scored by evaluating an objective function associated with the engineered system and using the possible input value combination and a corresponding plurality of actions. The action is selected from the corresponding plurality of actions, the selection being based at least in part on scores of the plurality of predictions. A rule specifying a corresponding set of one or more rule conditions that is met when the possible input value combination is matched and a corresponding action associated with the rule as a selected action is generated. The generated set of rules to be stored or further processed is output.

Abstract: A method for an automatic generation of industrial process graphics includes: receiving engineering data, device data, and sensor data of an industrial plant having a plurality of field devices, the engineering data, device data, and sensor data being assigned to a single or multiple field devices; extracting field device information for each field device of a plurality of the field devices from the assigned engineering data, device data, and sensor data; and generating a plurality of process graphics for each field device of the plurality of the field devices. The plurality of process graphics for each field device covers a plurality of different abstraction levels of the industrial plant.

Abstract: The present application provides an energy management system and method, electronic device, electronic apparatus, and nonvolatile processor. The method includes: performing prediction computation based on at least one type of the received power supply information, power storage information, and power outage information of the electronic device for at least one moment, and outputting at least one of a data bitwidth instruction, a start instruction or a write strategy instruction, or/and QoS prediction information; and performing energy management on operations of the processor based on the at least one instruction, or/and the QoS prediction information. In the present application, it can be ensured that the operations of the processor is matched with the expected energy thereof, and the QoS can be matched with the minimum QoS requested in advance.

Abstract: A method and apparatus for estimating and/or controlling mercury emissions in a steam generating unit. A model of the steam generating unit is used to predict mercury emissions. In one embodiment of the invention, the model is a neural network (NN) model. An optimizer may be used in connection with the model to determine optimal setpoint values for manipulated variables associated with operation of the steam generating unit.

Abstract: A method for determining an output value having a known relationship to an input value with a predicted value includes the step of first training a predictive model with at least one output for a given set of inputs that exist in a finite dataset. Data is then input to the predictive model that is within the set of given inputs. Thereafter, a prediction is made of an output from the predictive model that corresponds to the given input such that a predicted output value will be obtained which will have associated therewith the errors of the predictive model.

Abstract: A method of dispatching a plurality of electrical generation systems that are controlled by a central monitoring and control system. The central system determines the order in which the distributed generators are deployed based upon several variables, such as maintenance costs, operating costs, and operating performance. The determination of the start order is based upon a significant number of variables that vary from one make and model of generator to another. The system and method of the present invention utilizes fuzzy logic to determine the order in which the generators are dispatched based upon the multiple variables available.

Abstract: A system and method is provided for monitoring the operating condition of a pump by evaluating fault data encoded in the instantaneous current of the motor driving the pump. The data is converted to a frequency spectrum which is analyzed to create a fault signature having fault attributes relating to various fault conditions associated with the pump. The fault signature is then input to a neural network that operates in conjunction with a preprocessing and post processing module to perform decisions and output those decisions to a user interface. A stand alone module is also provided that includes an adaptive preprocessing module, a one-shot unsupervised neural network and a fuzzy based expert system to provide a decision making module that operates with limited human supervision.

Abstract: Power industry boiler tube failures are a major cause of utility forced outages in the United States, with approximately 41,000 tube failures occurring every year at a cost of $5 billion a year. Accordingly, early tube leak detection and isolation is highly desirable. Early detection allows scheduling of a repair rather than suffering a forced outage, and significantly increases the chance of preventing damage to adjacent tubes. The instant detection scheme starts with identification of boiler tube leak process variables which are divided into universal sensitive variables, local leak sensitive variables, group leak sensitive variables, and subgroup leak sensitive variables, and which may be automatically be obtained using a data driven approach and a leak sensitivity function. One embodiment uses artificial neural networks (ANN) to learn the map between appropriate leak sensitive variables and the leak behavior.

Abstract: A method for analyzing and displaying process states of a technical plant includes enabling simultaneous, coherent assessment and display of relevant process variables of the plant by evaluating relevant process variables with regard to one another through the use of a neural analysis on the basis of self-organizing maps, by making a topology-producing projection of data of the relevant process variables onto a neural map. The current process courses are plotted as trajectories on the map. Evaluation in the sense of a diagnosis can be carried out either visually or in an automated manner.

Abstract: The invention relates to a method for controlling process events of a technical plant. In order to permit a simultaneous and coherent assessment of relevant process variables of the plant, it is proposed to use a neural analysis on the basis of self-organizing neural maps to evaluate the relevant process variables in relation to one another by realizing a topology-maintaining nonlinear projection of data from the relevant process variables onto a multidimensional neural map.

Abstract: A method for building a model of a system includes first extracting data from a historical database (310). Once the data is extracted, a dataset is then created, which dataset involves the steps of preprocessing the data. This dataset is then utilized to build a model. The model is defined as a plurality of transforms which can be utilized to run an on-line model. This on-line model is interfaced with the historical database such that the variable names associated therewith can be downloaded to the historical database. This historical database can then be interfaced with a control system to either directly operate the plant or to provide an operator an interface to various predicted data about the plant. The building operation will create the transform list and then a configuration step is performed in order to configure the model to interface with the historical database. When the dataset was extracted, it is unknown whether the variables names are still valid.

Abstract: Power industry boiler tube failures are a major cause of utility forced outages in the United States, with approximately 41,000 tube failures occurring every year at a cost of $5 billion a year. Accordingly, early tube leak detection and isolation is highly desirable. Early detection allows scheduling of a repair rather than suffering a forced outage, and significantly increases the chance of preventing damage to adjacent tubes. The instant detection scheme starts with identification of boiler tube leak process variables which are divided into universal sensitive variables, local leak sensitive variables, group leak sensitive variables, and subgroup leak sensitive variables, and which may be automatically be obtained using a data driven approach and a leak sensitivity function. One embodiment uses artificial neural networks (ANN) to learn the map between appropriate leak sensitive variables and the leak behavior.

Abstract: The invention relates to a process diagnosis system and a method for the diagnosis of processes and states in a technical process, in particular for the diagnosis of a power station process. The invention is a structured multi-agent system which corresponds to the process and has a plurality of autonomous diagnostic agents. The diagnostic agents in each case contain a neural network, with whose aid a reference behavior of process components that are to be monitored can be learned. In addition, automatic adaptation to a new reference behavior is also possible by the diagnostic agents.

Abstract: A process control system determines optimal trajectories (input controls) using multiresolutional analysis of acquired data. In contrast to conventional control systems, the present control system does not use a predetermined mathematical model or algorithm to define the process in terms of a plurality of variables. Rather, the present system acquires system data and stores the data in a multiresolutional data structure. A knowledge base is created which can be searched at varying levels of resolution for determining optimal process trajectories. The continual addition of data to the data structure allows for continual top-down refinement of the determined trajectories and bottom-up improvement and updating of the system representation.

Abstract: A command value decision unit capable of solving a problem of a conventional system in that when using a static fuel consuming characteristic model obtained by the least squares method as a fuel consuming characteristic model, it is sometimes difficult to decide the output command value P.sub.m * of each plant at high accuracy owing to the deviation of collected data and the response delay of the plants. The command value decision unit generates an output decision model of each plant by extracting items that affect the fuel consumption at the present time from among the items of the fuel consuming characteristic model of each plant, which is formed by taking account of hysteresis characteristics of the plant, and decides the output command value of the plant on the basis of the output decision model.

Abstract: The automated integrated input-output control system apparatus. A versatile industrial control apparatus that can be utilized as a modular unit or integrated into industrial machinery or tools to provide an apparatus and method for programmable control of industrial tools, industrial controls and manufacturing operation processes. The automated integrated input-output control system apparatus includes the following components: a control-power mechanism housing, a sensor, input attachment devices, output attachment device, and a control system.