Described herein are systems and methods of alarm rationalization for an industrial control system. This can comprise building a model of the industrial control system, wherein the model includes components that are monitored or controlled by the industrial control system and alarms associated with the components; training the model by applying one or more machine learning algorithms against a historical database of alarms for the industrial control system; and applying the trained model against the industrial control system for alarm management of the industrial control system.

Disclosed are embodiments for automatically resolving faults in a complex network system. Some embodiments monitor one or more of system operational parameter values and message exchanges between network components. A machine learning model detects a fault in the complex network system, and an action is selected based on a cause of the fault. After the action is applied to the complex network system, additional monitoring is performed to either determine the fault has been resolved or additional actions are to be applied to further resolve the fault.

A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

The present disclosure provides a computer-implemented method for ranking one or more control schemes for controlling peak loading conditions and abrupt changes in energy pricing of one or more built environments associated with renewable energy sources. The computer-implemented method includes analysis of a first set of statistical data, a second set of statistical data, a third set of statistical data, a fourth set of statistical data and a fifth set of statistical data. Further, the computer-implemented method includes identification and execution of the one or more control schemes. In addition, the computer-implemented method includes scoring the one or more control schemes by evaluating a probabilistic score. Further, the computer-implemented method includes ranking the one or more control schemes to determine relevant control schemes for controlling real time peak loading conditions and abrupt changes in energy pricing associated with the one or more built environments.

A method for identifying a cause of a machine operating anomaly including creating a reduced order model (ROMs) for a digital twin model of a selected machine type and feeding current data from a deployed machine into the ROM. The method can include comparing a current output from the selected ROM with a measured output from the current data and determining that an operating anomaly exists when the difference between the current output and the measured output exceeds a selected anomaly threshold. The cause of the operating anomaly can be identified by feeding the current data into a plurality of fault models, wherein each fault model includes a particular component failure, comparing a fault model output from each of the plurality of fault models with the measured output from the current data, selecting the fault model with the fault model output most closely matching the measured output, and displaying the identified component failure associated with the selected fault model as the cause of the operating anomaly.

Techniques are provided herein for creating well-balanced computer-based reasoning systems and using those to control systems. The techniques include receiving a request to determine whether to use one or more particular data elements, features, cases, etc. in a computer-based reasoning model (e.g., as data elements, cases or features are being added, or as part of pruning existing features or cases). Conviction measures are determined and inclusivity conditions are tested. The result of comparing the conviction measure can be used to determine whether to include or exclude the feature, case, etc. in the model and/or whether there are anomalies in the model. A controllable system may then be controlled using the computer-based reasoning model. Examples controllable systems include self-driving cars, image labeling systems, manufacturing and assembly controls, federated systems, smart voice controls, automated control of experiments, energy transfer systems, health care systems, cybersecurity systems, and the like.

A method of analyzing events for an electrical system includes: receiving event stream(s) of events occurring in the electrical system, the events being identified from captured energy-related signals in the system; analyzing, an event stream(s) of the events to identify different actionable triggers therefrom, the different triggers including a scenario in which a group of events satisfies one or more predetermined triggering conditions; analyzing, over time, the different actionable triggers to identify a combination of occurring and/or non-occurring actionable triggers which satisfies a predefined trigger combination condition and an analysis time constraint; and in response to the observation of the combination, taking one or more actions to address the events. The analysis time constraint can be a time period duration and/or sequence within which time-stamped data of events in the event stream(s) and the associated actionable triggers are considered or not considered in the analysis to identify the combination.

A method for parameterizing an anomaly detection method, which takes a multiplicity of sensor data points as a basis for performing a density-based cluster method, including a) mapping each sensor data point in a data space into a pixel data point in a pixel space, b) reproducing at least one operation of the density-based cluster method in the data space by means of at least one pixel operation in the pixel space, c) receiving at least one parameter value for each parameter of the density-based cluster method, d) applying the at least one pixel operation in accordance with the parameter values to the pixel data points e) outputting a cluster result in visual form in the pixel space, and f) providing the received parameter values for the anomaly detection method, and an assistance apparatus for parameterizing an anomaly detection apparatus that performs the anomaly detection method.

There is provided a system for monitoring a screening machine, comprising a vibration sensor configured to record a vibration response of a screen fabric of the screening machine; and a signal processing device for digitally processing and evaluating the vibration response. In this connection, the signal processing device comprises an adaptive algorithm which is based on the methods of artificial intelligence, is related to vibration responses of one or several comparative screen fabrics and is adapted to characterize the vibration response recorded by the vibration sensor. Furthermore, a method for monitoring a screening machine is presented.

A system and method for unsupervised root cause analysis of machine failures. The method includes analyzing, via at least unsupervised machine learning, a plurality of sensory inputs that are proximate to a machine failure, wherein the output of the unsupervised machine learning includes at least one anomaly; identifying, based on the output at least one anomaly, at least one pattern; generating, based on the at least one pattern and the proximate sensory inputs, an attribution dataset, the attribution dataset including a plurality of the proximate sensory inputs leading to the machine failure; and generating, based on the attribution dataset, at least one analytic, wherein the at least one analytic includes at least one root cause anomaly representing a root cause of the machine failure.