The present invention refers to a continuous flow engine monitoring and controlling method including an improved process to adapt the existing system to deviation detected to provide an improved management and control system increasing the overall benefit provided by such continuous flow engine. Furthermore, the present invention refers to a system being adapted to perform such method. Additionally, the present invention refers to a computer program product being utilized to realize such method. Furthermore, the present invention refers to a use of such means to improve the utilization of such continuous flow engine.

A method of servicing equipment, the method comprising: recording information associated with servicing of the equipment in view of a workscope at a first location; sending the recorded information to a node; receiving service input from a third party located at a second location different from the first location, the third party having prepared the service input in response to the recorded information on the node; and performing the service using the received service input.

An edge device for use in a system for monitoring a vehicle-borne probe includes a first communication interface configured to receive sensed data related to a characteristic of a heating element of a first probe, a core application module configured to host a plurality of core applications, a dynamic application module configured to host a plurality of dynamic applications, and a processing unit configured to implement the plurality of core applications on the sensed data. The plurality of core applications includes a coarse data processing application configured to monitor and analyze the sensed data to generate a first data output.

According to one or more embodiments of the disclosure, a device in a network obtains parameters for entropy testing of industrial equipment that controls a physical process. Entropy is added to commands sent to the industrial equipment during the entropy testing. The device receives packets that were generated during the entropy testing of the industrial equipment and include sensor data regarding the physical process. The device determines whether the sensor data is inconsistent by analyzing the sensor data using a machine learning model that models the physical process. The device initiates a corrective measure, when the sensor data is determined to be inconsistent.

Methods, apparatus, systems, and articles of manufacture to obtain diagnostic information for a system are disclosed. An example apparatus includes vehicle interface circuitry to obtain information corresponding to a detected problem of a vehicle, cloud interface circuitry to obtain, via a network communication, an output of a machine learning model executed based on the information, the output to indicate (a) a component associated with the detected problem, and (b) a probability associated with the component, instruction generation circuitry to generate instructions for performing a test on the component, and user interface control circuitry to cause the instructions to be displayed on a mobile device.

A method that includes obtaining a sensor reading from a sensor installed inside an underground vault and determining whether the sensor reading is indicative of an alarm state. When the sensor reading is indicative of the alarm state, the method obtains at least one new reading and determines whether the sensor reading includes sensor drift based at least in part on the at least one new reading. The alarm state is established when the sensor reading is determined not to include sensor drift. The sensor drift is removed when the sensor reading is determined to include sensor drift.

To efficiently instruct maintenance workers if failure reports having different contents are issued in a short time period. The control part of the maintenance work instruction system, if a failure report is received from a monitored apparatus to which the first timer is not set, sets the first value to the first timer. After that, during a time period until a value of the first timer comes, if a failure report is received from an identical monitored apparatus, the control part of the maintenance work instruction system sets the second value shorter than the first value to the second timer. Then the control part of the maintenance work instruction system aggregates received failure reports during a time period after the first failure report until a lapse of time defined by the first value or a lapse of time defined by the second value in the second timer, whichever comes first.

An abnormality detection system configured to detect abnormal communication includes a first electronic control unit, a plurality of second electronic control units, a plurality of connector connection portions, and a processor. The connector connection portions are provided on a communication path between the first electronic control unit and the second electronic control units. Each connector connection portion includes a first connector portion and a second connector portion. The processor is configured to determine that, when abnormal communication occurs, one of the connector connection portions that is experiencing abnormal communication with all the second electronic control units connected to the second connector portion and that includes the second connector portion connected to the largest number of second electronic control units is abnormal.

A method and a system for controlling a lot risk score based dynamic lot measurement on the basis of equipment reliability index are provided. The method for controlling a measurement, according to an embodiment of the present invention, calculates an equipment reliability index of specific equipment for a specific process in semiconductor manufacturing, calculates a risk score of the specific equipment for the specific process on the basis of an equipment reliability index, and determines, on the basis of the risk score, whether to measure a semiconductor product processed by the specific equipment for the specific process. Therefore, differential quality monitoring and management is possible according to the equipment reliability index, a measuring instrument can be efficiently used, quality and yield can be improved through timely measurement, and management convenience can be increased through automatic and dynamic lot measurement control.

Multi-metric artificial intelligence (AI)/machine learning (ML) models for detection of anomalous behavior of a machine/system are disclosed. The multi-metric AI/ML models are configured to detect anomalous behavior of systems having multiple sensors that measure correlated sensor metrics such as coolant distribution units (CDUs). The multi-metric AI/ML models perform the anomalous system behavior detection in a manner that enables both a reduction in the amount of sensor instrumentation needed to monitor the system's operational behavior as well as a corresponding reduction in the complexity of the firmware that controls the sensor instrumentation. As such, AI-enabled systems and corresponding methods for anomalous behavior detection disclosed herein offer a technical solution to the technical problem of increased failure rates of existing multi-sensor systems, which is caused by the presence of redundant sensor instrumentation that necessitates complex firmware for controlling the sensor instrumentation.