Diagnosis of Incipient Fault Based on Sliding-Scale Resampling Strategy and Improved Deep Autoencoder

Yang Jing; Yang Yanxi; Xie Guo

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Diagnosis of Incipient Fault Based on Sliding-Scale Resampling Strategy and Improved Deep Autoencoder

机译：基于滑动规模重采样策略和改进的深度自动化器诊断初期故障

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Currently, the fault diagnosis with balanced data and distinct characteristics has received mass concern, and the related research achievements are remarkable. However, because of the weakness and scarcity of incipient fault signals, the diagnosis of incipient fault commonly existing in industrial systems is still an intractable problem. In order to solve the problem, an incipient fault diagnosis method based on a sliding-scale resampling strategy and improved sparse autoencoder with multi-particle noise addition (MpNA-SAE) is proposed in this paper. Firstly, the original time domain signals are preprocessed, and a sliding-scale resampling strategy is designed to construct the balanced sample sets. Secondly, a multi-particle noise addition strategy and an adaptive loss function are designed, and then an improved sparse autoencoder with multi-particle noise addition (MpNA-SAE) fault diagnosis model is constructed to identify the fault pattern and determine the severity degree. Thirdly, a diagnostic performance evaluation criterion is proposed to quantify the application range of the model. Finally, the effectiveness and practicability of the proposed method are verified by incipient artificial damage and real fault experiments, respectively.

机译：目前，具有平衡数据和不同特性的故障诊断得到了群众关注，相关的研究成果显着。然而，由于初始故障信号的弱点和稀缺性，工业系统中通常存在的初期故障的诊断仍然是一个难以解决的问题。为了解决问题，本文提出了一种基于滑动级重采样策略和改进具有多粒子噪声加法（MPNA-SAE）的初始故障诊断方法和改进的稀疏自动探剂。首先，原始时域信号是预处理的，并且设计了滑动级重采样策略以构建平衡样本集。其次，设计了多粒子噪声加法策略和自适应损耗功能，然后构造具有多粒子噪声添加（MPNA-SAE）故障诊断模型的改进的稀疏自动频率以识别故障模式并确定严重程度。第三，提出了一种诊断性能评估标准来量化模型的应用范围。最后，通过初始的人工损伤和实际故障实验验证了所提出的方法的有效性和实践性。

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来源
《IEEE sensors journal》 |2020年第15期|8336-8348|共13页
作者
Yang Jing; Yang Yanxi; Xie Guo;
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作者单位

Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China|Tianshui Normal Univ Sch Mechatron & Automot Engn Tianshui 741000 Peoples R China;

Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China;

Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China;

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正文语种 eng
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Fault diagnosis; Feature extraction; Vibrations; Rolling bearings; Sensors; Time-domain analysis; Noise measurement; Deep autoencoder; fault diagnosis; incipient fault; multi-particle noise addition; multiple constraint; regularization term; sliding-scale resampling;

机译：故障诊断;特征提取;振动;滚动轴承;传感器;时间域分析;噪声测量;深度自动探测器;故障诊断;初始故障;多粒子噪声添加;正规化术语;滑块缩写;

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5. Artificial intelligence applications in the diagnosis of power transformer incipient faults. [D] . Wang, Zhenyuan. 2000

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6. A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion [O] . Wenfeng Gong, Hui Chen, Zehui Zhang, 2019

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7. Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network [O] . Muhammad Sohaib, Jong-Myon Kim 2018

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Diagnosis of Incipient Fault Based on Sliding-Scale Resampling Strategy and Improved Deep Autoencoder

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