A cell controller of the present application includes: a machine information reception part that receives at least one of alarm information on manufacturing machines and status information on the manufacturing machines, and receives physical layout information on the manufacturing machines; a classification part that classifies the received physical layout information into a plurality of groups; and a display part that displays an abnormality status of the manufacturing machines for each of the groups of the classified physical layout information.

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.

A learning diagnosis apparatus performs learning from failure data to create a diagnostic model, and stores a model, a failure cause part, and sensor data of the equipment in a rare case data table when the number of cases of the failure cause part of the equipment is less than a predetermined number. Then, based on the diagnostic model created by a learning unit, an estimated probability of causing a failure is calculated for each part of the equipment in which a failure has occurred. Based on the rare case data table, a sensor data match rate between sensor data of the equipment in which the failure has occurred and past sensor data of the model of the equipment is calculated. Then, the calculated sensor data match rate for each part of the equipment in which the failure has occurred is displayed.

A method and device improve efficiency, depth and reliability of diagnosing technical condition of highly complex objects. The technical effect is achieved by signals received from a diagnosed object (DO). These signals are transformed into a technical condition initial estimate vector for the DO elements. Then, technical condition estimates for the DO elements are specified through a cyclical process and by using a reverse logical model and a direct logical model. A technical condition vector estimate is formed after the cyclical process is completed, and its variable components are used for deciding on technical condition of the diagnosed object elements. The device comprises an interface unit which inputs serve for connecting to the DO data outputs, a measuring unit, an initial estimate forming unit, a switching unit, a reverse triplex logical model and a direct triplex logical model, a result interpretation unit and a control unit.

In a packaging system including a packaging machine and at least one component provided in addition to the packaging machine, the packaging system includes a display and a computer unit. The system is configured to display a performance graphic on the display. The computer unit includes a fault analysis section that is configured to detect a stop or output deficit of the packaging system and, when a stop or an output deficit is detected, to determine, from a list of error sources stored in a memory, an error source causing the stop or output deficit so that the determined error source is shown on the display.

A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.

The present disclosure relates to a control system (2) for a vehicle. The control system is configured to receive a request to initiate a diagnostic conversation, and attempt to initiate the requested diagnostic conversation while the vehicle is in a sleep state. The control system is further configured to determine one or more target participants that are required for participation in a requested diagnostic conversation, to determine an on-board energy status of the vehicle, and to energise the target participants without energising the entire vehicle and initiate the requested diagnostic conversation in dependence on the on-board energy status of the vehicle being sufficient to conduct the requested diagnostic conversation.

The present disclosure provides a method for anomaly classification for an industrial control system (ICS) communication network based on statistical learning and deep learning. This method designs LSTM deep learning structure parameters and performs modeling analysis based on a large amount of traffic data during normal operation of the ICS communication network; based on real-time communication traffic data thresholds generated by a SARIMA online statistical learning model, designs correlated algorithms to analyze a numerical relationship between background traffic and real-time traffic; and classifies ICS communication network anomalies according to an ICS network anomaly classification algorithm. In the present disclosure, an ICS test network range involving virtual and physical devices and a test platform in Zhejiang Province are used for experimental analysis, a physical simulation platform is built in a laboratory environment for validation, and detailed examples are provided to verify the reliability and accuracy of the algorithm.

A method for identifying, classifying, and sending notification of an electric arc furnace's (EAF) anomalies to improve the EAF efficiency. The method includes the steps of establishing baseline state measurements of the EAF, receiving new state measurements of the EAF and statistically testing the new state measurements against the baseline state measurements. The method further includes the steps of identifying as an anomaly a failed statistical test, classifying the identified anomaly and sending notification of the classified anomaly to a configurable list of recipients.

A method for monitoring the operation of a turbomachine controlled by a digital control system including at least one component, includes acquiring operating state information relating to the state of at least one component; determining, depending on the state information acquired, a current degraded configuration in which at least one of the components has failed; determining a classification of the current degraded configuration using at least one classification table stored in a storage device, the classification tables associating with at least one degraded configuration one classification expressing the level of criticality of the degraded configuration, the tables being obtained by calculating a conditional probability of a predefined anticipated event from the probability of occurrence of elementary events relating to a failure of one of the components; and estimating an operating time permitted for the turbomachine depending on the classification determined for the current degraded configuration.