The present technology is generally directed to systems and methods for inspecting an asset in a communication-denied environment. The present technology can include receiving, via an electronic device, an image of a dashboard of the asset; determining, based on the image, a working mode and one or more status identifiers of the asset; transmitting the working mode and the one or more status identifiers to an inspection system; and/or receiving information regarding the status of the asset based on the working mode and one or more status identifiers.

A predictive maintenance system and method for intelligent manufacturing equipment is provided. The predictive maintenance system includes a first-stage predictive maintenance module, a second-stage predictive maintenance module, and a maintenance decision module. The first-stage predictive maintenance module includes an acquisition module, a human-computer interaction module, a calculation module, and a storage module. The second-stage predictive maintenance module includes a communication module, a setting module, and a prediction module. The maintenance decision module is configured to receive a first-stage remaining service life calculated by the first-stage predictive maintenance module and a second-stage remaining service life predicted by the second-stage predictive maintenance module, and determine a predictive maintenance strategy of the intelligent manufacturing equipment according to the first-stage remaining service life and the second-stage remaining service life. The present disclosure may reduce unexpected shutdown, reduce the costs of operation and maintenance, and improve the efficiency of operation and maintenance.

The present disclosure relates to techniques for detecting cyber-faults in industrial assets. Such techniques may include obtaining an input dataset from a plurality of nodes of industrial assets and predicting fault nodes in the plurality of nodes by inputting the input dataset to a one-class classifier. The one-class classifier may be trained on normal operation data obtained during normal operations of the industrial assets. Further, the cyber-fault detection techniques may include computing a confidence level of cyber fault detection for the input dataset using the one-class classifier and adjusting decision thresholds based on the confidence level for categorizing the input dataset as normal or including cyber-faults. The predicted fault nodes and the adjusted decision thresholds may be used for detecting cyber-faults in the plurality of nodes of the industrial assets.

An apparatus for detecting a fault state of an aircraft is provided. The apparatus accesses a training set of flight data for the aircraft. The training set includes observations of the flight data, each observation of the flight data includes measurements of properties selected and transformed into a set of features. The apparatus builds a generative adversarial network including a generative model and a discriminative model using the training set and the set of features, and builds an anomaly detection model to predict the fault state of the aircraft. The anomaly detection model is trained using the training set of flight data, simulated flight data generated by the generative model, and a subset of features from the set of features. The apparatus deploys the anomaly detection model to predict the fault state of the aircraft using additional observations of the flight data.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that can adjust operations of a manufacturing plant based on an assessment of risk to the plant's operations posed by the conditions and/or operations of the different devices in the manufacturing plant. Methods may include obtaining, using a set of sensors, a set of current operational characteristics for a plurality of plant devices in a manufacturing plant. For a particular plant device, a set of risk factors corresponding to a failure of the particular plant device can be analyzed. Based on the set of risk factors, an overall risk posed by the particular plant device to operations of the manufacturing plant can be determined. Based on the overall risk, one or more operations of the manufacturing plant can be adjusted.

The embodiments of the present disclosure provide method and Internet of Things (IoT) systems for maintaining and managing a storage and distribution station of smart gas. The method may be executed by a smart gas device management platform of the IoT system for maintaining and managing the storage and distribution station of smart gas. The method may include: obtaining gas tank data and gas tank environmental data of a target gas tank of a gas storage and distribution station; obtaining image data of the target gas tank, and predicting gas tank aging data of the target gas tank based on the image data; predicting, based on the gas tank aging data, the gas tank data, and the gas tank environmental data, gas tank damage data of the target gas tank; and determining a maintenance plan of the target gas tank based on the gas tank damage data.

A client-server system that performs machine learning based information fusion to predict part failure likelihood is described. The system receives transactional data pertaining to replacement of, and sensor data pertaining to duty cycle of, one or more parts. The system trains a first machine learning model, using the transactional data as training data, to extract a plurality of concepts corresponding to the information present in unstructured text in the transactional data. The system also trains a second machine learning model, using the sensor data and the extracted plurality of concepts, to predict part failure likelihood of the one or more parts. The system determines the part failure likelihood of the one or more parts by providing new transactional data and new sensor data to the trained machine learning models.

A method detects a manipulation of an exhaust gas system, in which measured values are sent to an external checking unit by a control unit of the exhaust gas system. In the external checking unit, an evaluation of the exhaust gas system as “manipulated” or as “not manipulated” takes place. Model values are formed in the control unit, which are at least partially formed from the measured values which are sent to the external checking unit. The external checking unit takes the model values and the measured values into consideration in the evaluation of the exhaust gas system.

A method for analyzing time series sensor data of a physical system represented by a process graph retrieves sensor data streams from stored sensor time series data. Each of the sensor data streams comprises a sequence of time-value pairs and is associated with a sensor identifier, a time offset, and a sampling period. A metric data stream is produced from the retrieved sensor data streams in accordance with a stored physics model of the physical system. Producing the metric data stream includes i) synchronizing the sensor data streams by adjusting time offsets of the sensor data streams and adding interpolated values and times to the sensor data streams to produce synchronized streams with equal sampling periods; and ii) performing a point-wise computation over values of the sensor data streams in accordance with the physics model.

A method for generating an individual model may be for use in a test procedure for a rail vehicle entry system, the model having a nominal curve describing a normal state of the entry system. The generation of the individual model for the entry system makes it possible, during a subsequent assessment or examination of a current condition of the entry system, to give more detailed information than, for example, when a model generically generated on a test rig may be used. The method includes at least reading and/or receiving, and generating operations. During reading, a surroundings signal may be read, which represents information on surroundings of the entry system. During receiving, a process signal may be received, which represents process information of the entry system generated during operation of the entry system. During generating, the model may be generated using the surroundings signal and/or the process signal.