Some embodiments of the present invention intend to provide a method and an apparatus for diagnosing health state of a 3D printer which collects collection data in a 3D printing process by using sensors attached to the 3D printer (for example, an acceleration sensor and an acoustic emission sensor), extracts feature elements of the sensor data, applies machine learning to an equipment health state diagnosis model based on the extracted feature elements, and thereby enables diagnosing equipment health state of 3D printer components in an objective and consistent manner.

Systems and methods for boiler regulation are disclosed. The system can receive boiler data from a boiler and compare the boiler data to a normal operating range to detect an abnormality. Based on a plurality of rules, the system can identify an anticipated root cause and at least one corrective action. Based on the at least one corrective action, the system can generate and/or output instructions for the boiler to perform the at least one corrective action. The system can display an indication of the abnormality and/or the at least one corrective action.

A method for predictive maintenance of equipment via a distribution chart is disclosed. Peak values are extracted based on a change in an amount of energy required for performing a work process by the equipment in a normal state, a distribution chart of the extracted peak values is constructed, and an abnormal symptom of the equipment is predictively detected in advance based on a change in distribution probability of a detection section having a low distribution probability and somewhat high risk in the constructed distribution chart thereof such that maintenance and replacement of the equipment are induced to be carried out at an appropriate time. Thus, an enormous monetary loss caused by a failure in the equipment may be prevented in advance.

A portable system for testing or demonstrating an agricultural implement includes a case (104) carrying a power supply (206), a plurality of electrical couplers (120) configured to receive wiring harnesses associated with test devices, a simulator module (214) configured to simulate at least one operating parameter of the agricultural implement on which test devices are carried, and a control system. The control system includes a graphical user interface (110) and processing circuitry operably electrically coupled to the graphical user interface (110) and to the wiring harnesses. The processing circuitry is configured to monitor and display information pertaining to operation of the test devices. A method for testing or demonstrating an agricultural implement includes connecting a test device an electrical coupler of the portable system, sending a control signal to the test device, and monitoring performance of the test device with the control system. The control signal is based at least in part on the data input.

A method for correlating data from sensors includes receiving sensor information from a plurality of sensors of an industrial operation. Sensor information from component sensors is used for functionality of a component of the industrial operation and sensor information from additional sensors monitor conditions of a portion of the industrial operation different from the component. The method includes deriving, using the sensor information, correlations between component sensors and additional sensors and deriving a baseline signature from the sensor information and the correlations. The baseline signature encompasses a range of normal operating conditions. The method includes identifying an abnormal operating condition based on a comparison between additional sensor information and the baseline signature. The sensor information is used differently for functionality of the component than for deriving the correlations and baseline signature and identifying the abnormal operating condition. The method includes sending an alert with the abnormal operating condition.

Cavitation detection systems and methods may include receiving sensor data from one or more data sources; translating a first format of the sensor data to a second format of the sensor data; transmitting the second format of the sensor data; receiving one or more requests for the second format of the sensor data; transmitting one or more responses that are responsive to the one or more requests; and triggering one or more actions based on the one or more responses.

A cloud diagnostic system and a method of operating the same to diagnose or predict potential fault conditions in an appliance includes connecting the appliance to a cloud diagnostics server directly over a network or through a service computer, receiving, at the cloud diagnostics server, appliance data from the appliance, analyzing the appliance data using one or more machine learning models on the cloud diagnostics server to diagnose or predict the potential fault conditions along with a confidence score, adjusting the confidence score based on historical fault data from a historical guidance service, and communicating the potential fault conditions to the appliance, to a user of the appliance, or to a field technician.

An electronic apparatus is provided. The electronic apparatus includes a storage storing error-related information of an external electronic apparatus, and a processor configured to obtain first error-related information with respect to a target time interval and second error-related information with respect to a standard time interval including the target time interval and time intervals other than the target time interval, from the storage, obtain frequency information for each number of error occurrences with respect to the target time interval based on the first error-related information and frequency information for each number of error occurrences with respect to the standard time interval based on the second error-related information, and compare the frequency information for each number of error occurrences with respect to the target time interval with the frequency information for each number of error occurrences with respect to the standard time interval to identify an error occurrence level with respect to the target time interval.

A test fixture for testing aerosol provision devices may include a housing, a plurality of testing modules disposed at the housing where each of the testing modules includes a cavity configured to receive a portion of an aerosol provision device, and processing circuitry operably coupled to the testing modules. Each of the testing modules may be configured to interface with an assembly of a respective one of the aerosol provision devices to transition the assembly between an initial state and a transitioned state during a functional test controlled by the processing circuitry. The processing circuitry may be configured to conduct the functional test of at least two of the testing modules simultaneously.

Disclosed are systems, methods, and devices for simulating human manual input for devices using capacitive touchscreens. In one embodiment, the system comprises a test fixture, wherein the test fixture comprises a matrix of tubes, each tube being coated with a conductive coating; and a camera located under the matrix and configured to record the capacitive touchscreen of the device under test. The system further includes a tablet to receive images from the camera and display a visual representation of the capacitive touchscreen of the device under test, wherein the tablet is configured to receive a plurality of touch events; update the visual representation of the capacitive touchscreen of the device under test in response to the plurality of touch events; and generate a simulation of touch events, the simulation representing interaction with the device under test. The system further includes a workstation communicatively coupled to the tablet and configured to receive the simulation from the tablet device; and transmit the simulation to the test fixture to enable the execution of the simulation on one or more additional devices under test.