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.

A vibration reliability calculation apparatus 10 is provided with an image acquisition unit 11 that acquires time-series images of an object that are output by an image capturing apparatus that shoots the object, and a reliability calculation unit 12 that calculates, for a vibration waveform of the object that is derived from a result of comparing one image and another image that constitute the acquired time-series images, a reliability level indicating a reliability of the vibration waveform.

This disclosure relates generally to a system and method to estimate an operational risk associated with one or more failures in at least one unit of a process plant. There is a continuous stream of operational data of several variables such as temperature, pressure, etc. Detections are defined in terms of acceptable/unacceptable ranges of parameters over a finite period and operating load of the unit. Often, these predefined parameters must be within a specified range based on operating condition of the process plant and when the measured parameters go beyond, a failure is detected. A risk priority number is estimated from number of occurrences of failure mode, average percentage change from dynamic limits with severity and degree of correlation with detectability from operational data and dynamic limits. Herein, operational risk associated with failure modes can be calculated and updated from time to time automatically from the stream of operational data.

This disclosure relates generally to a system and method to estimate an operational risk associated with one or more failures in at least one unit of a process plant. There is a continuous stream of operational data of several variables such as temperature, pressure, etc. Detections are defined in terms of acceptable/unacceptable ranges of parameters over a finite period and operating load of the unit. Often, these predefined parameters must be within a specified range based on operating condition of the process plant and when the measured parameters go beyond, a failure is detected. A risk priority number is estimated from number of occurrences of failure mode, average percentage change from dynamic limits with severity and degree of correlation with detectability from operational data and dynamic limits. Herein, operational risk associated with failure modes can be calculated and updated from time to time automatically from the stream of operational data.

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 system for simultaneously measuring the indentation hardness properties, span properties, and resilience properties of a mattress includes a first indentation means and a second indentation means, and means for urging the first indentation means and the second indentation means into the mattress with a predetermined force, and also includes laser means for projecting a laser line configured to map, preferably by photographic triangulation, the amplitude, shape, and time-dependency of the resultant deflection of the mattress surface between the first indentation means and the second indentation means. A method for simultaneously measuring the indentation hardness properties, span properties, and resilience properties of a mattress is also provided.

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.