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首页> 外文期刊>IEEE sensors journal >A Hybrid Temporal Feature for Gear Fault Diagnosis Using the Long Short Term Memory
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A Hybrid Temporal Feature for Gear Fault Diagnosis Using the Long Short Term Memory

机译：使用长短短期内存进行齿轮故障诊断的混合时间特征

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摘要

The vibration of the rotating machinery for condition monitoring in gear fault detection is a popular area of study. Reliable improvements to the rotating machinery can be obtained by enhancing the machine condition monitoring. The automatic detection of a gear fault at an early stage is required to guarantee a reliable and robust rotating machinery system. In this paper, a novel method of gear fault diagnosis is proposed based on extracting a computational cheap hybrid hand-crafted feature set including the Gamma Tone Cepstral Coefficient (GTCC) and the Mel-Frequency Cepstral Coefficient (MFCC), extracted temporally from the vibration signal. The vibration signal faults have a temporal nature, so the Long Short-Term Memory (LSTM) classifier is adopted because it is suitable for time series signals. To evaluate the proposed model, a ten-fold cross validation approach is applied to two different datasets. The results obtained show that the adopted features and the LSTM classifiers are effective for gear fault detection. Additionally, the performance of the fusion of 14 coefficients for both the GTCC and MFCC exceed the state-of-the-art performance for gear fault detection and for those which use learned features using a pre-trained model.
机译：齿轮故障检测中旋转机械的旋转机械的振动是一种流行的研究领域。通过增强机器状态监测，可以获得对旋转机械的可靠改进。需要在早期阶段自动检测齿轮故障以确保可靠且坚固的旋转机械系统。在本文中，基于提取包括γ色调谱系码（GTCC）和熔融频率倒数系数（MFCC）的计算廉价的混合手工制作特征组，提出了一种齿轮故障诊断方法，从振动中提取。信号。振动信号故障具有时间性性质，因此采用了长的短期存储器（LSTM）分类器，因为它适用于时间序列信号。为了评估所提出的模型，将十倍的交叉验证方法应用于两个不同的数据集。得到的结果表明，采用的功能和LSTM分类器对齿轮故障检测有效。另外，GTCC和MFCC两者的14系数融合的性能超过了齿轮故障检测的最新性能，以及使用预先训练的模型使用学习功能的最新性能。

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