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首页> 外文期刊>IEEE sensors journal >In Situ Motor Fault Diagnosis Using Enhanced Convolutional Neural Network in an Embedded System
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In Situ Motor Fault Diagnosis Using Enhanced Convolutional Neural Network in an Embedded System

机译：在嵌入式系统中使用增强型卷积神经网络的原位电机故障诊断

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摘要

Convolutional neural networks (CNNs) are one of the most efficient deep learning techniques and have been widely used in motor fault diagnosis. However, most of them are implemented in desktop computers to process off-line signals. In this paper, an in situ motor fault diagnosis method is proposed by implementing an enhanced CNN model into a designed embedded system consisting of a Raspberry Pi and a signal acquisition and processing circuit. To the best of our knowledge, this topic has not been investigated yet in the literature. First, the hardware, algorithms, and heterogeneous computing framework are introduced in detail. Then, the method effectiveness and efficiency are validated on a motor test rig. In particular, as the resources in an embedded system are limited, the algorithm accuracy and execution time are investigated. The robustness of the designed system is further validated by analyzing the motor signals with different signal-to-noise ratios. The contributions of this paper include the following: 1) a heterogeneous computing framework is proposed and an integrated embedded system is designed; 2) the performance of the enhanced CNN in embedded system is validated; and 3) the proposed method provides a solution to realize in situ motor fault diagnosis on a small-size, flexible, and convenient handheld device by exploiting the artificial intelligence technique.
机译：卷积神经网络（CNNS）是最有效的深度学习技术之一，已广泛用于电机故障诊断。但是，大多数在桌面计算机中实现以处理离线信号。在本文中，通过将增强的CNN模型进入由覆盆子PI和信号采集和处理电路组成的设计嵌入式系统来提出in原位电机故障诊断方法。据我们所知，本主题尚未在文献中进行调查。首先，详细介绍硬件，算法和异构计算框架。然后，在电动机试验台上验证了方法有效性和效率。特别地，随着嵌入式系统中的资源受到限制，研究了算法精度和执行时间。通过以不同的信噪比分析电动机信号，进一步验证了设计系统的鲁棒性。本文的贡献包括以下内容：1）提出了异构计算框架，并设计了集成的嵌入式系统; 2）验证了嵌入式系统中增强CNN的性能; 3）所提出的方法提供了一种解决方案，通过利用人工智能技术来实现对小型，灵活，方便的手持设备的原位电机故障诊断。

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