The present invention is directed to a system for testing printed circuit boards. The system is configured to test the simultaneously test a multiplicity of printed circuit boards. The system examines the electrical characteristics of a printed circuit board and is operable to identify if a printed circuit board meets a desired characteristic.

A method for calibrating an electronic assembly during a manufacturing process is provided, including the steps: determining a calibration value for the assembly which for a predefined input value gives a deviation between an actual output value output by the assembly and a predefined desired output value, transmitting the calibration value to the assembly, and storing the calibration value in the assembly, wherein the calibration value of the assembly is determined by a machine learning method executed in a calibration device, and the machine learning method is trained by training data, which include historical calibration values of a plurality of assemblies of the same type and parameters of assemblies of the same type, which are dependent on the manufacturing process and/or express physical properties.

Method for detecting rail switches degradation and failures, the method having the steps of applying a Discrete Wavelet Transform to stored measurement data relative to a switch move to be analysed and obtaining a feature vector associated with said switch move and including Discrete Wavelet Transform coefficients delivered by the applied Discrete Wavelet Transform; comparing the obtained feature vector with feature vectors associated with rail switch moves previously obtained and associating each to a respective health class among several given health classes; and determining a health class for the said switch move to be analyzed by selecting one of the health classes associated with the previously obtained feature vectors based upon the comparison step.

A method for detecting anomalies in a spectrogram, spectrum or signal using a detection module having a first machine learning submodule, a second machine learning submodule and a comparison submodule is provided. The method includes: receiving the spectrogram, spectrum or signal as measured data by the detection module; generating filtered data based on the measured data by the first submodule; generating predicted data based on the measured data and/or the filtered data by the second submodule; and determining that the spectrogram, spectrum or signal includes an anomaly if the filtered data deviates from the measured data and/or the predicted data deviates from the measured data and/or the filtered data by at least a predetermined amount.

A semiconductor device inspection apparatus includes: a light sensor that detects light from a semiconductor device as a DUT to which an electric signal has been input; an optical system that guides light from the semiconductor device to the light sensor; and a control device electrically connected to the light sensor. The control device includes: a data reading unit that reads mask data indicating a mask layout of the semiconductor device; a search unit that searches for a position of a transistor in the semiconductor device on the basis of polygon data of a gate layer of the semiconductor device included in the mask data; a setting unit that sets the searched position of the transistor as an optical measurement target position; and a measurement unit that performs optical measurement for the set optical measurement target position to acquire a measurement result.

The present invention is directed to a system for testing printed circuit boards. The system is configured to test the simultaneously test a multiplicity of printed circuit boards. The system examines the electrical characteristics of a printed circuit board and is operable to identify if a printed circuit board meets a desired characteristic.

An audio and vibration based power distribution equipment condition monitoring system and method is provided. The system includes an intelligent audio analytic (IAA) device and a sensing assembly. The IAA device has computer executable instructions such as an audio data processing algorithms configured to identify and predict impending anomalies associated with one or more power distribution equipment using one or more neural networks.

Solar array fault detection, classification, and localization using deep neural nets is provided. A fault-identifying neural network uses a cyber-physical system (CPS) approach to fault detection in photovoltaic (PV) arrays. Customized neural network algorithms are deployed in feedforward neural networks for fault detection and identification from monitoring devices that sense data and actuate each individual module in a PV array. This approach improves efficiency by detecting and classifying a wide variety of faults and commonly occurring conditions (e.g., eight faults/conditions concurrently) that affect power output in utility scale PV arrays.

A computer-implemented method of analyzing an electrical wiring harness assembly comprises receiving, via a processing element, a test result associated with the electrical wiring harness assembly; determining, via the processing element, a probable error type based on the test result; retrieving from a memory element, via the processing element, a plurality of natural language terms associated with the probable error type, each of the plurality of natural language terms having a corresponding phrase order value; and reporting, via a user interface, each of the plurality of natural language terms in an order according to the phrase order values.

A method, system, and computer program product for detecting and monitoring a signal is provided. The method includes detecting an alignment point for a periodic signal segment of a periodic signal generated by an apparatus being monitored for standard functionality In response, the apparatus is activated from a period prior to the alignment point to an end point of the periodic signal segment and a first point of the periodic signal segment is located. Likewise, a second point of an additional periodic signal segment of the periodic signal is located. The periodic signal is normalized based on results of locating the first point and the second point.