Patent classifications
G01R31/00
NONLINEAR AUTOREGRESSIVE EXOGENOUS (NARX) MODELLING FOR POWER ELECTRONIC DEVICE MODELLING
Systems, methods, computer-readable media, techniques, and methodologies are disclosed for performing fault detection and prediction for power electronics and switching devices for power electronics. Systems and methods can determine, using a machine learning model, a prediction for a value of a first switching parameter of a switching device, the prediction based on the present value of a second switching parameter of a switching device and a prior value of the first switching parameter. Systems and methods disclosed herein can further determine a residual comprising the difference between the prediction and an actual value of the switching parameter, generate a test statistic based on the residual, and compare the test statistic to a first threshold value. Systems and methods disclosed herein can determine the presence of a fault in the switching device based on a comparison of the test statistic to a threshold value.
Transfer system for microelements
A transfer system for transferring multiple microelements to a receiving substrate includes a main pick-up device, a testing device, and first and second carrier plates. The testing device includes a testing platform, a testing circuit, and multiple testing electrodes electrically connected to the testing circuit. The main pick-up device is operable to releasably pick up the microelements from the first carrier plate and position the microelements on the testing electrodes. The testing device is operable to test the microelements to distinguish unqualified ones of the microelements from qualified ones. The main pick-up device is operable to release the qualified ones of the microelements to the receiving substrate.
Transfer system for microelements
A transfer system for transferring multiple microelements to a receiving substrate includes a main pick-up device, a testing device, and first and second carrier plates. The testing device includes a testing platform, a testing circuit, and multiple testing electrodes electrically connected to the testing circuit. The main pick-up device is operable to releasably pick up the microelements from the first carrier plate and position the microelements on the testing electrodes. The testing device is operable to test the microelements to distinguish unqualified ones of the microelements from qualified ones. The main pick-up device is operable to release the qualified ones of the microelements to the receiving substrate.
Transformer failure identification and location diagnosis method based on multi-stage transfer learning
A transformer failure identification and location diagnosis method based on a multi-stage transfer learning theory is provided. Simulation is set up first, a winding parameter of a transformer to be tested is calculated, and a winding equivalent circuit is accordingly built. Different failures are configured for the equivalent circuit, and simulation is performed to obtain a large number of sample data sets. A sweep frequency response test is performed on the transformer to be tested, and detection data sets are obtained. Initial network training is performed on simulation data sets by using the transfer learning method, and the detection data sets are further trained accordingly. A failure support matrix obtained through diagnosis is finally fused. The multi-stage transfer learning theory is provided by the disclosure.
Transformer failure identification and location diagnosis method based on multi-stage transfer learning
A transformer failure identification and location diagnosis method based on a multi-stage transfer learning theory is provided. Simulation is set up first, a winding parameter of a transformer to be tested is calculated, and a winding equivalent circuit is accordingly built. Different failures are configured for the equivalent circuit, and simulation is performed to obtain a large number of sample data sets. A sweep frequency response test is performed on the transformer to be tested, and detection data sets are obtained. Initial network training is performed on simulation data sets by using the transfer learning method, and the detection data sets are further trained accordingly. A failure support matrix obtained through diagnosis is finally fused. The multi-stage transfer learning theory is provided by the disclosure.
POWER SUPPLY CONTROL DEVICE, TEST METHOD, AND COMPUTER PROGRAM
A power supply control device controls a supply of power by switching a FET on or off. A current that rises when a current flowing through the FET rises flows through a resistor circuit. A drive circuit makes a notification when a voltage across both ends of the resistor circuit reaches a voltage greater than or equal to a reference voltage. A microcomputer instructs an application circuit to apply a voltage to the resistor circuit. As a result, the application circuit applies a voltage greater than or equal to the reference voltage to the resistor circuit. After instructing the application circuit to stop applying the voltage to the resistor circuit, the microcomputer determines whether or not the drive circuit is making the notification.
METHOD FOR DIAGNOSING FAILURE OF CURRENT BREAKING DEVICE AND ENERGY STORAGE APPARATUS
A method for diagnosing failure of a current breaking device 21A included in a power supply system 12 of a vehicle 1 includes: a supply step of supplying power to a first electric load 11 and a first energy storage apparatus 13 by a power supply apparatus 14; a command step of commanding the current breaking device 21A to perform cutoff while power is supplied from the power supply apparatus 14 to the first electric load 11 and the first energy storage apparatus 13; and a determination step of measuring a charge current of a secondary battery 20A by a current sensor 21B while the cutoff is commanded to the current breaking device 21A, and determining presence or absence of failure of the current breaking device 21A based on a measured current value.
Measurement device and method for measuring a device under test
A measurement device is described that comprises a measurement unit configured to perform measurements on an electric signal of a device under test while applying at least one measurement parameter for performing the measurements. The measurement device has an integrated direct current source configured to power the device under test. The measurement device also comprises a monitoring unit configured to monitor at least one monitoring parameter of the direct current source. The measurement device has a control unit configured to control the measurement parameter. Further, a method for measuring a device under test is described.
SYSTEMS AND METHODS OF TESTING MEMORY DEVICES
A memory device includes a first memory block. The first memory block includes a first memory sub-array and a first interface portion disposed next to the first memory sub-array. The first memory block further includes a plurality of first interconnect structures electrically coupled to the first memory sub-array through the first interface portion, and a second plurality of interconnect structures configured to electrically couple a corresponding one of the plurality of first interconnect structures to a transistor. The memory device further includes a first test structure and a second test structure disposed next to the first memory block, each configured to simulate electrical connections of the plurality of second interconnect structures. The first and second test structures are electrically coupled to each other and are each electrically isolated form the first memory block.
Delta-difference amplifier circuit for restraint control module
A system for diagnosing a squib loop in a restraint control module. The system may include a first amplifier, a capacitor, a second amplifier. The first amplifier may have a first input connected to a first side of the squib and a second input connected to a second side of the squib. The output of the first amplifier may generate an output voltage corresponding to the voltage drop across the squib. The capacitor may be connected in series with the output of the first amplifier and the output of the first amplifier may be connected to a first side of the capacitor. The second amplifier having a first input connected to a second side of the capacitor. A second input of the second amplifier may be connected to a reference voltage. The second amplifier may be configured with a feedback loop to generate a gain output.