Disclosed herein are systems and methods for assessing an impact of malicious software causing a denial of service of components of industrial automation and control systems (IACS). In one aspect, an exemplary method comprises, generating a configuration of the IACS on a testing device based on specifications, obtaining a set of investigated software, where the set includes at least one sample of one malicious software, testing the generated configuration using the received set of investigated software, identifying occurrences of denials of service of the components of the testing device which are used to simulate the generated configuration, determining an impact of the malicious software on the generated configuration, and a degree of degradation of a performance of the generated configuration of IACS, and pronouncing a verdict as to a danger of the malicious software for the generated configuration of IACS based on the determined impact of the malicious software.

This disclosure relates to the technical field of automobiles and software and discloses an interface circuit and a method and apparatus for interface communication thereof. In the interface circuit, a micro-control unit has an output interface connected to a first conversion unit connected to a USB HOST interface and a USB SLAVE interface and a second conversion unit connected to an HDMI interface, and is configured to control the first conversion unit to be set in a HOST mode to communicate with the USB HOST interface when receiving a trigger signal from the USB HOST interface, the first conversion unit to be set in an SLAVE mode to communicate with the USB SLAVE interface when receiving a trigger signal from the USB SLAVE interface, and the second conversion unit to output the TMDS signal to communicate with the HDMI interface when receiving a trigger signal from the HDMI interface.

Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for modifying physical emissions sources based on a plurality of simulations of different scenarios utilizing a modified gradient descent model. Specifically, the disclosed system utilizes the modified gradient descent model to generate emissions value modifications for physical emissions sources corresponding to an entity based on a set of constraints and target emissions values. The disclosed system runs a plurality of simulations to generate modified target emissions values, utilizing the modified gradient descent model, by modifying source attributes of the physical emissions sources according to a plurality of probability distributions representing source attributes of the physical emissions sources. The disclosed system then compares the initial target emissions values to the modified target emissions values determined from the simulations to generate action recommendations for modifying the physical emissions sources.

In some embodiments, the system is directed to an autonomous inspection system for electrical grid components. In some embodiments, the system collects electrical grid component data using an autonomous drone and then transmits the inspection data to one or more computers. In some embodiments, the system includes artificial intelligence that analysis the data and identifies electrical grid components defects and provides a model highlighting the defects to a user. In some embodiments, the system enables a user to train the artificial intelligence by providing feedback for models where defects or components are not properly identified.

A method for identifying a fault of at least one mechanical machine, including causing a first plurality of sensors coupled to a corresponding first plurality of mechanical machines to acquire a first plurality of sets of signals emanating from the first plurality of mechanical machines, the first plurality of mechanical machines sharing at least one characteristic, supplying at least the first plurality of sets of signals of the first plurality of mechanical machines to a pre-existing fault classifier previously trained to automatically identify faults of a second plurality of mechanical machines based on signals emanating therefrom and previously acquired by a second plurality of sensors, the second plurality of sensors being of a different type than the first plurality of sensors, the second plurality of mechanical machines sharing the at least one characteristic, modifying the pre-existing fault classifier by employing transfer learning, based at least on the first plurality of sets of signals of the first plurality of mechanical machines, thereby providing a modified fault classifier, applying the modified fault classifier to at least one additional set of signals acquired by at least one sensor of the first plurality of sensors and emanating from at least one given mechanical machine sharing the at least one characteristic, the modified fault classifier being configured to automatically identify at least one fault of the at least one given mechanical machine based on the at least one additional set of signals, and providing a human sensible output, by an output device, including at least identification of the fault of the at least one given mechanical machine, at least one of a repair or maintenance operation being performed based on the human sensible output.

A vehicle diagnostic system and method utilizing a plurality of diagnostic data corpuses. A first corpus comprises conventional and well-known diagnostic data useful in a broad range of common service tasks. A second corpus comprises less well-known diagnostic data useful in less common service tasks. One or more specialty corpuses may comprise obscure, protected, or emerging diagnostic data for the last-common tasks specific to particular manufacturers of the vehicle or its components. The second corpus and one or more specialty corpuses may offer access only to users that have acquired sufficient authorization.

According to certain aspects of the disclosure, a computer-implemented method may be used for detecting health status of an environmental control system. The method may include receiving aircraft data of an aircraft and receiving flight data of an aircraft. Calculating a predicted performance of the aircraft based on the received aircraft data and the received flight data and generating at least one model scalar or residual, wherein the at least one model scalar or residual is generated based on the aircraft data of the aircraft. Identifying at least one pattern from the at least one model scalar or residual and classifying the at least one pattern into at least one of a plurality of classifications. Identifying a failure of modes or components from the classifications and transmitting a maintenance report once the failure of modes or components is identified.

A monitoring method, the purpose of which is, when a loss of power is detected in an aircraft engine, to generate an alarm in the form of a single message displayed on a display screen in the cockpit, in order to indicate if the level of damage suffered by the engine is critical or not. The steps implemented are based on alarm signals transmitted by a central processing unit of the engine and also on alarm signals transmitted by a diagnostic device for the onboard systems of the aircraft, in order to take account of both the situation of the engine and also the situation of the systems surrounding the engine which can be affected by damage to an engine.

Methods and systems for fault identification and mitigation in an engine system. A state observer obtains current state information from the engine system, and a feature calculator uses data obtained from the state observer to calculate one or more feature indicators, which are monitored by a health estimator for the occurrence of a change using one or more change probability models. When the health estimator identifies a change, a fault isolator determines a component of the engine system that is subject to fault or health deterioration.

Various embodiments described herein relate to an enterprise data management dashboard. In this regard, a request to generate a dashboard visualization related to one or more assets is received. The request includes an asset descriptor describing the one or more assets. In response to the request, aspects of aggregated operational technology data within a knowledge graph data structure are correlated to provide one or more insights associated with the one or more assets. Additionally, the dashboard visualization is provided to an electronic interface of a computing device. The dashboard visualization includes visualization data for the one or more insights associated with the knowledge graph data structure.