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 control apparatus 1 includes a control unit 21 that controls execution of a workflow including a handling execution process when an alarm indicating a failure is occurred, and an instruction unit that instructs the control unit 21 to stand by execution of the handling execution process when the handling execution process is capable of being executed before a monitoring period started in response to occurrence of an alarm indicating the failure or a recovery expires, and when the monitoring period expires, execute the handling execution process in a case where an alarm that is occurred most recently indicates the failure, and cancel the execution of the handling execution process in a case where the alarm that is occurred most recently indicates the recovery.

A plurality of potential fault scenarios are accessed, wherein a given potential fault scenario of the plurality of potential fault scenarios has at least one corresponding root cause, and a representation of the given potential fault scenario comprises a don't care value. An actual fault scenario from telemetry received from a monitored system is generated. The actual fault scenario is matched against the plurality of potential fault scenarios. One or more matched causes are output as one or more probable root cause failures of the monitored system.

A system and computer-implemented method for identifying and repairing suboptimal operation of a machine, the computer-implemented method including: monitoring sensory input data related to an industrial machine; analyzing, using an unsupervised machine learning model, the monitored sensory inputs, wherein the output of the unsupervised machine learning model includes at least one indicator; identifying, based on the at least one indicator, at least one behavioral pattern related to the industrial machine, wherein each of the at least one behavioral pattern is indicative of at least one suboptimal operation of the industrial machine; selecting at least one corrective action based on the at least one behavioral pattern; and performing the at least one corrective action on the industrial machine.

The present disclosure discloses a control method of industrial Internet of Things (IoT) based on abnormal identification. The IoT includes: an obtaining unit, which is configured to obtain a first machining parameter; a detection unit, which is configured to obtain real-time image data when the first machining parameter is abnormal; an extraction unit, which is configured to obtain a keyframe and obtain a second machining parameter; a judgment unit, which is configured to determine an abnormal cause based on the first machining parameter and the second machining parameter; and a communication unit, which is configured to transmit the abnormal cause to a user terminal through a service platform.

A diagnostic tool, a computer software program, and a method for determining an interruption in a plant with at least one device includes determining a plant status at a recording time by recording at least one plant status value and transmitting the plant status to the diagnostic tool, determining a device status at a recording time by recording at least one device status value and transmitting the device status to the diagnostic tool, assigning a shared time base to the transmitted device status and the transmitted plant status, and correlating the transmitted device status and the transmitted plant status by coordinating the device status and the plant status on the shared time base.

An information processing system includes a terminal apparatus that sends the received machine-type identification information and state history information received from the monitoring target apparatus by near field communication, an information processing apparatus that receives the machine-type identification information and the state history information from the terminal apparatus, and a storage apparatus that accumulates machine-type identification information identifying a machine type of a monitoring target apparatus and normal-state history information in an associated manner. When the information processing apparatus receives the machine-type identification information and state history information about a certain monitoring target apparatus from the terminal apparatus, the information processing apparatus compares the normal-state history information accumulated in the storage apparatus in association with the machine-type identification information and the received state history information, judges whether an operation state of the monitoring target apparatus is normal, and sends a judgment result to the terminal apparatus.

A system for predicting HVAC equipment failure includes an actuator and a controller. The actuator is coupled to the HVAC equipment and configured to drive the HVAC equipment between multiple positions. The actuator includes a processing circuit configured to collect internal actuator data characterizing an operation of the actuator and a communications circuit coupled to the processing circuit. The communications circuit is configured to transmit the internal actuator data outside the actuator. The controller is configured to provide control signals to the actuator and receive the internal actuator data from the actuator. The controller includes a failure predictor configured to use the internal actuator data to predict a time at which the HVAC equipment failure will occur.

An intermittent failure diagnostic system that, in addition to a failure detection of a conventional failure threshold, appropriately sets an intermittent diagnostic threshold at which a probability of false-positive detection of the intermittent failure is equal to a probability of false-positive detection of the conventional failure, more accurately detects the intermittent failure, suppresses an unprepared system down and improves the reliability, may be used with an electric power steering apparatus.

A monitoring system for monitoring the temperature and vibration of equipment, comprising a central digital computer, a MESH communication network, wherein the network feeds signals to the central digital computer, a plurality of heating elements for heating the equipment, temperature/vibration sensors adapted to measure the temperature of the equipment, wherein each sensor is adapted to provide a signal representing the temperature/vibration of the piece of equipment to which the sensor is associated, to the network, wherein each temperature/vibration sensor can also be used to control the electric heaters, a temperature sensor that monitors the ambient temperature of the facility, and current transducers associated with the heaters, to monitor the energy use and current loss of the heaters, wherein the central computer uses the data it receives from the other elements of the monitoring system to determine when the equipment is not at the correct temperature/vibration and diagnoses the reason why.