Abstract: A power supply health check system for checking a health state of an under-test power supply is provided. The under-test power supply supplies power to a main board which has a voltage signal during operation. The health check system includes a detecting module, a deep learning model, and a processing unit. The detecting module is electrically connected to the main board to detect the voltage signal and convert the voltage signal into a digital signal. The deep learning model is established by using frequency-domain voltage data of a plurality of healthy power. The processing unit is configured to: collect the digital signal and store the digital signal as under-test time-domain voltage data; convert the under-test time-domain voltage data into under-test frequency-domain voltage data; and calculate, based on the under-test frequency-domain voltage data and the deep learning model, a health indicator for determining the health state of the under-test power supply.

Abstract: A noise measuring device is provided. The noise measuring device includes a soundproof box, a sound receiving device, a holding device, and a driving device. The sound receiving device is disposed in the soundproof box. The holding device is disposed in the soundproof box and configured to hold a testing object. The driving device is connected with the soundproof box and configure to drive the soundproof box to rotate.

Abstract: A power supply health check system for checking a health state of an under-test power supply is provided. The under-test power supply supplies power to a main board which has a voltage signal during operation. The health check system includes a detecting module, a deep learning model, and a processing unit. The detecting module is electrically connected to the main board to detect the voltage signal and convert the voltage signal into a digital signal. The deep learning model is established by using frequency-domain voltage data of a plurality of healthy power. The processing unit is configured to: collect the digital signal and store the digital signal as under-test time-domain voltage data; convert the under-test time-domain voltage data into under-test frequency-domain voltage data; and calculate, based on the under-test frequency-domain voltage data and the deep learning model, a health indicator for determining the health state of the under-test power supply.

Abstract: A signal abnormality detection system and a method thereof are provided. The signal abnormality detection system includes a signal sensor and a computing device. The signal sensor generates a sample signal to be tested through sensing. The computing device is signal-connected to the signal sensor to receive the sample signal to be tested, perform a correction on the sample signal to be tested, and perform a time-frequency transform on a one-dimensional signal generated after the correction to generate a two-dimensional time-frequency signal. The computing device reconstructs the two-dimensional time-frequency signal by using an abnormality detection model to calculate a reconstructed difference value. The computing device performs comparison to determine whether the reconstructed difference value is greater than a detection threshold to determine whether the sample signal to be tested is an abnormal sample.

Abstract: A noise measuring device is provided. The noise measuring device includes a soundproof box, a sound receiving device, a holding device, and a driving device. The sound receiving device is disposed in the soundproof box. The holding device is disposed in the soundproof box and configured to hold a testing object. The driving device is connected with the soundproof box and configure to drive the soundproof box to rotate.

Abstract: The disclosure provides a signal detection method. The signal detection method includes: collecting initial data; pre-processing the initial data to obtain an original signal; reconstructing the original signal by using an optimized deep learning model, to generate a reconstructed signal; and comparing the original signal with the reconstructed signal, to determine whether there is an abnormality in the original signal. The disclosure further provides an electronic device using the signal detection method.