A mobile power supply fault detection method and system based on intelligent diagnosis

By performing baseline modeling, spectrum analysis, and multi-scale decomposition of voltage and current data from power banks, combined with a support vector machine model, the problem of insufficient accuracy in power bank fault detection in existing technologies is solved. This enables early identification and real-time monitoring of subtle anomalies, improving the accuracy and stability of fault detection.

CN120180335BActive Publication Date: 2026-07-24SHENZHEN ITC TECH CO LTD
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Patent Information

Application Number
CN202510324225.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-03-19
Publication Date
2026-07-24
Estimated Expiration
2045-03-19

AI Technical Summary

Technical Problem

Existing technologies lack in-depth analysis of electrical performance characteristics in mobile power bank fault detection, making it difficult to detect subtle or early abnormal signals, leading to missed or misdiagnosed faults, and lacking effective handling of hidden and intermittent abnormal fluctuations.

Method used

By collecting voltage and current data under normal operating conditions of mobile power banks, an electrical performance baseline dataset is generated. Time series features are extracted, and fast Fourier transform and wavelet transform analyses are performed to identify spectral features and multi-scale spectral data. Finally, a support vector machine model is used for fault prediction and monitoring.

Benefits of technology

It improves the sensitivity and accuracy of fault location, enables early warning of hidden faults, reduces safety hazards, and enhances the service life and operational stability of power banks.

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Abstract

The application relates to the technical field of electric fault detection, in particular to a mobile power supply fault detection method and system based on intelligent diagnosis, which comprises the following steps: collecting voltage and current data of a mobile power supply under a normal operation state, generating an electric performance baseline data set by recording the time stamp and corresponding electric performance indexes of the data; and extracting time sequence characteristics of the voltage and the current based on the electric performance baseline data set to obtain electric performance characteristic data. In the application, the voltage and current data of the mobile power supply under the normal state are collected to establish a baseline data set covering the time stamp and the electric performance indexes, time sequence characteristics such as peak value, average value and standard deviation are extracted to capture detailed information of electric performance changes; meanwhile, frequency spectrum characteristics are accurately extracted by means of fast Fourier transform to master the intensity and phase information of frequency components, and then wavelet transform is adopted to decompose the signal to obtain more abundant electric performance abnormal information under multiple scales.
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