AI辅助下动态声纹分析策略在电池组异常识别中的应用
Application of AI assisted dynamic voiceprint analysis strategy in battery pack anomaly recognition
范龙 1张见广1
作者信息
- 1. 黄河水利职业技术学院机械工程学院,河南 开封 475004
- 折叠
摘要
本文综述了人工智能(AI)辅助下的动态声纹分析策略在电池组异常识别中的应用创新情况.首先概述了声纹识别理论的基础,随后详细介绍了AI的理论框架,包括机器学习、深度学习等关键技术,以及这些技术如何在复杂数据处理、模式识别等方面展现出强大的能力.重点探讨了基于AI和声纹技术的电池组异常检测策略.通过集成高精度的声音采集设备、先进的信号处理技术以及优化的AI算法,该策略能够实时监测电池组运行过程中的声音变化,并利用动态声纹分析技术提取出关键的声音特征,进行异常模式的识别与分类.新技术不仅提高了异常识别的准确性和实时性,还能够有效应对电池组运行过程中可能出现的复杂多变的异常情况.
Abstract
This article summarizes the innovative application of AI assisted dynamic voiceprint analysis strategy in battery pack anomaly recognition.The article first outlines the foundation of voiceprint recognition theory,and then provides a detailed introduction to the theoretical framework of artificial intelligence(AI),including key technologies such as machine learning and deep learning,as well as how these technologies demonstrate powerful capabilities in complex data processing,pattern recognition,and other areas.The article focuses on exploring battery pack anomaly detection strategies based on AI and voiceprint technology.By integrating high-precision sound collection equipment,advanced signal processing technology,and optimized AI algorithms,this strategy can monitor the sound changes during the operation of the battery pack in real time,and use dynamic voiceprint analysis technology to extract key sound features for abnormal pattern recognition and classification.The new technology not only improves the accuracy and real-time performance of anomaly recognition,but also effectively addresses complex and variable abnormal situations that may occur during the operation of battery packs.
关键词
人工智能/声纹/异常检测Key words
artificial intelligence/voiceprint/anomaly detection引用本文复制引用
基金项目
河南省高等学校重点科研项目(18A880019)
河南省高等教育教学改革研究与实践项目(2021SJGLX666)
出版年
2024
国家科技期刊平台
NSTL
万方数据