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首页> 外文会议>IASTED International Conference on Artificial Intelligence and Soft Computing >ADVANCES IN AUTOMATIC DETECTION OF FAILURES IN ELECTRIC MACHINES USING AUDIO SIGNALS
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ADVANCES IN AUTOMATIC DETECTION OF FAILURES IN ELECTRIC MACHINES USING AUDIO SIGNALS

机译：使用音频信号自动检测电机故障的进步

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In this paper nonlinear chaotic features have been obtained from audio signals of different kinds of electric machines as a first step in order to develop a personal computer (PC) based artificial intelligence system for the fault identification and diagnosis of electric machines. These techniques can be applied in fault identification and diagnosis in industrial scenarios by mean of expert systems. Different nonlinear features (based on chaos theory) to detect changes of the audio signal were studied: maximal Lyapunov exponent, correlation dimension and correlation entropy. We also studied related measurement such as the time delay and the value of the first minimum of the mutual information function, the first zero of the autocorrelation function and Shannon entropy. We used different recordings from different scenarios (PC fans, an iron cutter and an electric drill).
机译：在本文中，已经从不同类型的电机的音频信号获得了非线性混沌特征作为第一步，以便开发基于个人计算机（PC）的人工智能系统，用于电机的故障识别和诊断。这些技术可以通过专家系统的平均值应用于工业场景的故障识别和诊断。研究了不同的非线性特征（基于混沌理论）以检测音频信号的变化：最大Lyapunov指数，相关维度和相关熵。我们还研究了相关的测量，例如相互信息函数的第一最小值的时间延迟和值，自相关函数和香农熵的第一零。我们使用不同场景（PC风扇，铁切割机和电钻）的不同录音。

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