Fault diagnosis of rolling bearing plays a vital role in identifying incipient failures and ensuring the reliable operation of the mechanical system. To improve the performance of the whole machine-fault-diagnosis system and meet the requirements of low cost, low consumption, high-reliability in industrial wireless sensor networks (IWSNs), a high-accuracy least-time-domain features fault diagnosis algorithm based on the BP neural network (BPNN) for IWSNs is proposed in this article. First, the hardware of wireless multifeatures extraction sensor node is designed, which performs local-processing features extraction of four-dimensional parameters and five dimensionless features of the vibration signal. Then, the bearing-fault classification based on mentioned characteristics is investigated in the proposed BPNN with different hidden layer nodes. Furthermore, we make the comparisons of bearing-fault classification accuracy in terms of varying number of dimensional features, dimensionless features, and the combination features, searching a least-time-domain mixture features selection strategy for ensuring high-fault classification accuracy and proving the effectiveness and feasibility of the proposed method by experiments on drivetrain diagnostics simulator system. This article is conducted to provide new insights into how to select the least time-domain features for high-accuracy fault diagnosis and further giving references to more IWSNs scenarios.

中文翻译：

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基于无线传感器网络的高精度最小时域混合特征机器故障诊断

滚动轴承的故障诊断对于识别早期故障并确保机械系统的可靠运行起着至关重要的作用。为了提高整个机器故障诊断系统的性能，并满足工业无线传感器网络（IWSN）中低成本，低功耗，高可靠性的要求，基于高精度最小时域特征的故障诊断算法本文提出了针对IWSN的BP神经网络（BPNN）。首先，设计了无线多特征提取传感器节点的硬件，对振动信号的二维参数和五个无量纲特征进行局部处理特征提取。然后，在提出的具有不同隐藏层节点的BPNN中，研究了基于上述特征的轴承故障分类。此外，我们根据尺寸特征，无量纲特征和组合特征的数量变化进行了轴承故障分类精度的比较，搜索了最小时域混合特征选择策略以确保高故障分类精度并证明了传动系统诊断仿真器系统的实验结果表明，该方法的有效性和可行性。本文旨在提供有关如何选择最小时域特征进行高精度故障诊断的新见解，并进一步为更多IWSN场景提供参考。搜索最小时域混合特征选择策略，以确保高故障分类的准确性，并通过在传动系统诊断模拟器系统上的实验证明了该方法的有效性和可行性。本文旨在提供有关如何选择最小时域特征进行高精度故障诊断的新见解，并进一步为更多IWSN场景提供参考。搜索最小时域混合特征选择策略，以确保高故障分类的准确性，并通过在传动系统诊断模拟器系统上的实验证明了该方法的有效性和可行性。本文旨在提供有关如何选择最小时域特征进行高精度故障诊断的新见解，并进一步为更多IWSN场景提供参考。