A novel motor fault diagnosis using only motor current signature is developed using a frequency occurrence plot-based convolutional neural network (FOP-CNN). In this study, a healthy motor and four identical motors with synthetically applied fault conditions—bearing axis deviation, stator coil inter-turn short circuiting, a broken rotor strip, and outer bearing ring damage—are tested. A set of 150 three-second sampling stator current signals from each motor fault condition are taken under five artificial coupling loads (0, 25%, 50%, 75% and 100%). The sampling signals are collected and processed into frequency occurrence plots (FOPs) which later serve as CNN inputs. This is done first by transforming the time series signals into its frequency spectra then convert these into two-dimensional FOPs. Fivefold stratified sampling cross-validation is performed. When motor load variations are considered as input labels, FOP-CNN predicts motor fault conditions with a 92.37% classification accuracy. It precisely classifies and recalls bearing axis deviation fault and healthy conditions with 99.92% and 96.13% f-scores, respectively. When motor loading variations are not used as input data labels, FOP-CNN still satisfactorily predicts motor condition with an 80.25% overall accuracy. FOP-CNN serves as a new feature extraction technique for time series input signals such as vibration sensors, thermocouples, and acoustics.

中文翻译：

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基于频率发生图的卷积神经网络在电机故障诊断中的应用

使用基于频率发生图的卷积神经网络（FOP-CNN），开发了仅使用电动机电流信号的新型电动机故障诊断。在这项研究中，测试了一个健康的电动机和四个具有综合应用故障条件的相同电动机-轴承轴偏差，定子线圈匝间短路，转子条断裂和轴承外圈损坏。在五个人工耦合负载（0％，25％，50％，75％和100％）下，从每个电动机故障条件中获取了一组150个3秒采样定子电流信号。采样信号被收集并处理成频率出现图（FOP），这些频率图随后用作CNN输入。首先，将时间序列信号转换为其频谱，然后将其转换为二维FOP。进行五重分层抽样交叉验证。当将电动机负载变化视为输入标签时，FOP-CNN可以以92.37％的分类精度预测电动机故障状况。它可以精确地分类和召回轴承轴偏差故障和健康状况，分别具有99.92％和96.13％的f分数。当没有将电动机负载变化用作输入数据标签时，FOP-CNN仍可以以80.25％的总体准确度令人满意地预测电动机状况。FOP-CNN是一种用于时间序列输入信号（例如振动传感器，热电偶和声学）的新特征提取技术。它可以精确地分类和召回轴承轴偏差故障和健康状况，分别具有99.92％和96.13％的f分数。当没有将电动机负载变化用作输入数据标签时，FOP-CNN仍可以以80.25％的总体准确度令人满意地预测电动机状况。FOP-CNN是一种用于时间序列输入信号（例如振动传感器，热电偶和声学）的新特征提取技术。它可以精确地分类和召回轴承轴偏差故障和健康状况，分别具有99.92％和96.13％的f分数。当没有将电动机负载变化用作输入数据标签时，FOP-CNN仍可以以80.25％的总体准确度令人满意地预测电动机状况。FOP-CNN是一种用于时间序列输入信号（例如振动传感器，热电偶和声学）的新特征提取技术。