A method for automatic diagnosis of a mechanical system of a group of mechanical systems sharing mechanical characteristics includes obtaining data relating to a vibration. The vibration-related data is acquired by a portable communications device configured to communicate with a remote processor. The processor automatically diagnoses the mechanical system by applying a relationship to the obtained vibration-related data. The relationship is based on sets of vibration-related data previously obtained from the mechanical systems. Each set of vibration-related data relates to vibrations of a mechanical system. The relationship is further based on sets of operation data previously obtained for mechanical systems of the group. Each set of operation data indicates a previous state of operation of a mechanical system. Each of the previous states of operation is associated with at least one of the previously obtained sets of vibration-related data.

An inspection tool and associated methods for testing physical and electrical properties of a perforating gun and sending the perforating gun to a wellbore site with at least one of an electrical property and a dimension that has been previously verified. The perforating gun may be received in a perforating gun holder positioned between a first connecting portion and second connecting portion. The first connecting portion may be moved from a first position to a second position at which the at least one of the electrical property and the dimension is measured. Upon verification that the at least one of the electrical property and the dimension is within a predetermined specification, the perforating gun may be sent to the wellbore site.

A diagnostic apparatus includes circuitry to determine a process number based on operation information that changes in accordance with an operation of a machine being a subject of diagnosis, and determine a state of the machine using corresponding one of a plurality of model parameters corresponding to the determined process number and state information that changes in accordance with the state of the machine. The process number indicates an order of a particular process in a plurality of processes executed by the machine.

A method for testing a technical system. Tests are carried out with the aid of a simulation of the system. The tests are evaluated with respect to a fulfillment measure of a quantitative requirement on the system and different error measures of the simulation. On the basis of the fulfillment measure and each of the error measures, a classification of the tests is carried out as either reliable or unreliable case by case. A selection among the error measures is made on the basis of a number of the tests classified as reliable.

An automatic robot control system and methods relating thereto are described. These systems include components such as a touch screen panel (“TSP”) robot controller for controlling a TSP robot, a camera robot controller for controlling a camera robot and an audio robot controller for controlling an audio robot. The TSP robot operates inside a TSP testing subsystem, the camera robot operates inside a camera testing subsystem, and the audio robot operates inside an audio testing subsystem. Inside the audio testing subsystem, an audio signals measurement system, using a bi-directional coupling, controls the operation of the audio robot controller. In this control scheme, a test application controller is designed to control the different types of subsystem robots. Methods relating to TSP, camera, and audio robots, and their controllers, taken individually or in combination, for automatic testing of device functionalities are also described.

A diagnostic service system includes one or plurality of factory monitoring systems configured to perform monitoring of at least one machine; a service center management device that is connected with the one or plurality of factory monitoring systems via a network; one or plurality of service centers that are connected with the service center management device; and a plurality of service terminals connected with one service center or each of the plurality of service centers via a service control. The plurality of service terminals are used by each responder capable of fault diagnosis of the machine, and when fault of a machine occurs, one of the plurality of service terminals is selected via the service center management device and the one service center or plurality of service centers.

Disclosed are a method and apparatus for detecting a fault, and a method and apparatus for training a model. The method includes: acquiring characteristic data and actual temperature of a first wind turbine among n wind turbines, wherein the characteristic data of the first wind turbine is intended to characterize a working state of the first wind turbine, and n is an integer greater than 1; acquiring a prediction temperature set by inputting the characteristic data of the first wind turbine into a temperature prediction model corresponding to each of the n wind turbines; and detecting, based on the predicted temperature set and the actual temperature of the first wind turbine, whether the first wind turbine encounters a fault. Compared with the related art which depends on the working experience of the staff, the technical solution according to the embodiments of the present disclosure can more accurately detect whether a wind turbine encounters a fault, and provide early warning in time, so as to reduce the failure rate of the wind turbine.

An equipment state monitoring device projects a plurality of pieces of actual measurement data indicating a state value of equipment to a dimensionless space in which a plurality of display shapes indicating a respective plurality of pieces of normal information indicating a normal state of the equipment are represented by a common shape, and estimates a distribution of a state of the equipment on the basis of the plurality of pieces of actual measurement data projected to the dimensionless space. Further, both or one of the normal information in the dimensionless space and the actual measurement data in the dimensionless space is corrected on the basis of the relationship between actual measurement data indicating the normal state of the equipment in the dimensionless space and the display shape indicating the normal information in the dimensionless space.

Rotating machinery is evaluated by calculating a boundary condition based on a measured value of a parameter related to an operating state of the rotating machinery, and calculating an evaluation value corresponding to the calculated boundary condition based on the reduced order model, during operation of the rotating machinery. The reduced order model is created based on a prediction model including a heat transfer model and a structural model of the rotating machinery for predicting an evaluation value of the rotating machinery corresponding to the boundary condition.

A machining accuracy diagnosing device includes a temperature regulation operating pattern setting unit, a cutting condition setting unit, a temperature information acquiring unit, and a machining accuracy influence amount predicting unit. The temperature regulation operating pattern setting unit sets an operating pattern of a temperature regulating unit. The cutting condition setting unit sets a scheduled machining start time and a scheduled machining end time. The temperature information acquiring unit acquires an influencing temperature of the temperature regulating unit on the machine body temperature and/or an air temperature outside the plant. The machining accuracy influence amount predicting unit predicts an influence amount of the temperature regulating unit on the machining accuracy based on the operating pattern, the scheduled machining start time and the scheduled machining end time, and at least one of the influencing temperature and/or the air temperature outside the plant and a set temperature of the temperature regulating unit.