Deep-Residual-Shrinkage-Networks

The deep residual shrinkage network is a variant of deep residual networks (ResNets), and aims to improve the feature learning ability from highly noise signals or complex backgrounds. Although the method is originally developed for vibration-based fault diagnosis, it can be applied to image recognition and speech recognition as well. The major innovation is the integration of soft thresholding as nonlinear transformation layers into ResNets. Moreover, the thresholds are automatically determined by a specially designed sub-network, so that no professional expertise on threshold determination is required.

The method is implemented using TensorFlow 1.0.1, TFLearn 0.3.2, and Keras 2.2.1, and applied for image classification. A small network with 3 residual shrinkage blocks is constructed in the code. More blocks and more training iterations can be used for a higher performance.

Abstract:

This paper develops new deep learning methods, namely, deep residual shrinkage networks, to improve the feature learning ability from highly noised vibration signals and achieve a high fault diagnosing accuracy. Soft thresholding is inserted as nonlinear transformation layers into the deep architectures to eliminate unimportant features. Moreover, considering that it is generally challenging to set proper values for the thresholds, the developed deep residual shrinkage networks integrate a few specialized neural networks as trainable modules to automatically determine the thresholds, so that professional expertise on signal processing is not required. The efficacy of the developed methods is validated through experiments with various types of noise.

Reference:

Minghang Zhao, Shisheng Zhong, Xuyun Fu, Baoping Tang, Michael Pecht, Deep residual shrinkage networks for fault diagnosis, IEEE Transactions on Industrial Informatics, 2020, 16(7): 4681-4690.

The paper has been cited over 900 times on Google Scholar.

https://scholar.google.com/citations?user=k82TzLwAAAAJ&hl=zh-CN

https://ieeexplore.ieee.org/document/8850096

http://homepage.hit.edu.cn/zhaominghang

Results:

(1) The performance on the manually noised Cifar10 dataset

In the DRSN_TFLearn.py, we manually add noise to the Cifar10 dataset, and get the results of a deep residual shrinkage network. Then, we delete lines 79-88 in DRSN_TFLearn.py, make it be a deep residual network, and get its results.

Methods	Deep residual shrinkage network	Deep residual network
Training accuracy	88.96%	87.78%
Validation accuracy	84.33%	83.99%

(2) The performance on the un-noised Cifar10 dataset

If we delete lines 25-27 in DRSN_TFLearn.py, the code will conduct a deep residual shrinkage network on the Cifar10 dataset without manually added noise. Then, if we delete lines 79-88 in DRSN_TFLearn.py, the code will conduct a deep residual network on the un-noised Cifar10 dataset.

Methods	Deep residual shrinkage network	Deep residual network
Training accuracy	90.28%	89.26%
Validation accuracy	85.87%	85.57%

Additional notes

There might be some problems in the Keras code. The TFLearn code is recommended for usage.

Thanks for the applications

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[288] 沈庆阳. 《第三章基于混合注意力残差收缩网络的R2D2模型》基于深度学习的指针式仪表自动识别与示数读取方法研究[D]. 天津理工大学, 2023.

[289] 程倩.《基于改进深度残差收缩网络的无线电大雾天气监测方法》基于深度学习的无线电大雾天气监测方法[D].兰州交通大学,2023.

[290] 马国轩.《第三章基于深度残差收缩网络的故障诊断方法》基于深度学习的机械故障诊断系统研究与实现[D].江苏大学,2023.

[291] 戴江涛.《第4章基于双流残差收缩网络的人体动作识别》基于深度学习的人体动作识别方法研究[D].沈阳工业大学,2022.

[292] 李伟龙.《第4章基于CBAM改进的残差收缩网络滚动轴承故障诊断方法》基于深度学习的滚动轴承故障诊断的方法研究[D].哈尔滨理工大学,2022.

[293] 张文鹏.《第四章基于改进深度残差收缩网络的故障诊断算法》基于深度学习的光纤电流互感器故障诊断方法研究[D].东南大学,2022.

[294] 张雅雯.《基于残差收缩网络的时间序列分类算法》基于残差网络的时间序列分类算法研究[D].北京交通大学,2020.

[295] 刘安顺.《基于深度残差收缩网络的生成对抗网络去运动模糊》基于生成对抗网络的图像去运动模糊算法研究[D].西华大学,2022.

[296] 侯梦军.《基于残差收缩注意力网络的单样本部分遮挡人脸识别》基于深度学习的单样本部分遮挡人脸识别研究[D].重庆邮电大学,2022.

[297] 曾宏伟.《第五章基于深度残差收缩网络的刀具磨损评估》钻削加工颤振监测、抑制及刀具磨损评估方法研究[D].上海交通大学,2021.

[298] 詹灿坚.《第四章基于SRP-残差收缩网络的混合气体定量分析》基于仿生嗅觉的混合气体定量分析[D].广东工业大学,2021.

[299] 陈玲玲.《第3章基于残差收缩网络的睡眠单通道脑电分期研究》基于深度神经网络的睡眠分期研究[D].哈尔滨工程大学,2022.

[300] 崔坤鹏.《基于残差收缩网络的脑图像配准方法》脑部核磁共振图像非刚性配准关键技术研究[D].郑州大学,2021.

[301] 陈辉.《基于残差收缩网络的姿态变化不敏感的纹理特征提取》姿态变化不敏感的足底识别算法研究[D].大连海事大学,2023.

[302] 陈文壮.《基于改进深度残差收缩网络的电机轴承故障诊断模型》基于深度迁移学习的电机轴承故障诊断方法研究[D].辽宁工程技术大学,2023.

[303] 徐威.《第3章基于多尺度深度残差收缩的AUV推进器故障诊断》基于深度学习的AUV推进器多信源融合故障诊断方法研究[D].江苏科技大学,2023.

[304] 贺才郡.《基于深度残差收缩网络的PQDS识别》基于深度学习的电能质量扰动识别及模型轻量化研究[D].华中科技大学,2023.

[305] 孙诗胜.《第四章基于改进深度残差收缩网络的轴承故障诊断研究》基于卷积神经网络的轴承故障诊断及寿命预测研究[D].石家庄铁道大学,2023.

[306] 刘伟洁.《第三章基于2D-DRSN的心律失常分类研究》&《第四章基于DRSN-Bi GRU的心律失常分类研究》基于深度学习的ECG心律失常分类研究[D].青海师范大学,2022.

[307] 杨一炫.《4.1 基于深度残差收缩网络的射频指纹识别》基于深度学习的通信个体辐射源识别研究[D].兰州交通大学,2023.

[308] 曾祥军.《基于混合卷积深度残差收缩网络的齿轮箱故障诊断》数据驱动的风电机组齿轮箱异常检测与故障诊断研究[D].山东大学,2022.

[309] 陆伟.《第5章 深度残差收缩网络与肌肉贡献度融合的肌力预测方法》基于肌电信号的人体上肢肌力预测方法研究[D].**科学技术大学,2023.

[310] 李大鹏.《基于Mobile Net V3和残差收缩网络的鸟声识别算法》自然场景下鸟鸣声识别算法研究[D].南京信息工程大学,2022.

[311] 张文琪.《第四章基于多尺度特征和残差收缩网络的多谱段图像匹配》基于CNN的多谱段图像匹配[D].贵州大学,2022.

[312] 王飞龙.《第4章融合深度残差收缩网络与胶囊网络的带噪声小样本图像分类模型》基于胶囊网络的复杂小样本图像分类研究[D].太原理工大学,2021.

[313] 李慧.《第四章基于多尺度残差收缩网络的遥感图像分类算法》结合面向对象与深度学习的高分辨率遥感图像分类研究[D].广东工业大学,2020.

[314] 陈润榕.《第四章基于深度残差收缩网络的轴承故障诊断方法》&《第五章基于混合域注意力的深度残差收缩网络的轴承故障诊断方法》基于深度学习的复杂工况下滚动轴承故障诊断方法研究[D].华南理工大学,2022.

[315] 李响.《第四章基于深度迁移残差收缩网络的故障诊断方法》基于深度迁移学习的轴承故障诊断方法研究[D].佛山科学技术学院,2022.

[316] 龚玉晓.《第四章基于改进深度残差收缩网络的心电信号分类算法》基于深度学习的心电信号自动分类算法研究[D].西安电子科技大学,2023.

[317] 张胜利.《第四章基于自适应深度残差收缩网络的雷达辐射源脉内调制样式识别》基于深度学习的雷达辐射源识别研究[D].国防科技大学,2022.

[318] 陈成雪.《第3章基于DRSN的行人检测算法》基于YOLOv4的交叉道路场景下的行人检测算法研究[D].沈阳师范大学,2023.

[319] 王云庆.《基于DRSN-BiLSTM刀具剩余使用寿命预测》基于信息融合的刀具磨损状态监测和剩余使用寿命预测[D].华东交通大学,2023.

[320] 唐震.《第二章基于深度残差收缩网络的辐射源个体识别方法》基于深度学习的辐射源个体识别技术研究与实现[D].南京信息工程大学,2023.

[321] 刘师良.《第4章基于IEMD和自适应残差收缩网络的轴承故障诊断》基于数据驱动和深度学习的滚动轴承故障诊断方法研究[D].青岛理工大学,2022.

[322] 胡博文.《第3章基于深度残差收缩网络的电力系统暂态稳定预测方法》基于深度学习的电力系统暂态稳定预测研究[D].贵州大学,2023.

[323] 冯骥.《第五章基于FDRSN-IBES-SVM的含DG配电网故障识别方法》基于多模型融合的含分布式电源配电网故障诊断研究[D].宁夏大学,2022.

[324] 续宗棠.《第三章基于注意力机制改进的脉冲残差收缩网络模型》&《第四章基于混合注意力机制的深度脉冲残差收缩网络模型》基于脉冲卷积神经网络的故障信号诊断[D].青岛大学,2023.

[325] 张睿琳.《基于深度残差收缩网络的帕金森脑电研究》&《5.1 可调Q因子小波变换与深度残差收缩网络结合的帕金森脑电研究》&《5.2 小波包变换与深度残差收缩网络结合的帕金森脑电研究》基于时频分析与深度学习的帕金森脑电分析[D].西北大学,2022.

[326] 史杨梅.《基于双通道阈值共享深度残差收缩网络的轴承故障诊断》基于声学信号的轴承故障诊断算法[D].**矿业大学,2022.

[327] 刘芯志.《第四章基于改进深度残差收缩网络的轻量级故障诊断方法》基于深度学习的滚动轴承故障诊断研究[D].湖南工业大学,2022.

[328] 杨惠.《第四章基于一维残差收缩网络的电能质量复合扰动识别方法》基于深度学习的电能质量扰动识别方法研究[D].东北石油大学,2022.

[329] 马浩然.《第四章基于Wide&Deep残差收缩网络的小样本学习》基于深度学习的刀具磨损自适应监测研究[D].华东师范大学,2023.

[330] 魏煦航.《基于DRSN的轴承健康状态评估建模》基于深度学习的印刷装备智能诊断系统研究与实现[D].北京印刷学院,2022.

[331] 郑琪.《第4章基于深度残差收缩网络的电梯轴承分段剩余寿命预测》基于数据特征挖掘与机器学习的电梯故障诊断与预警[D].浙江大学,2022.

[332] 李雪松.《第三章基于CWT-MDRSN的轴承故障诊断》基于深度学习的轴承故障诊断[D].青岛大学,2022.

[333] 文井辉.《第2章基于DRSN的轴承健康指标构建方法研究》基于改进BiLSTM的轴承剩余寿命预测方法研究[D].重庆邮电大学,2022.

[334] 方鹏.《第三章基于深度残差收缩网络的刀具磨损状态识别》基于多传感器信息融合的刀具磨损量监测系统研究[D].重庆交通大学,2021.

[335] 刘畅.《第四章基于DRSN-ViT网络的图像情感分类识别模型》基于视觉深度自注意力变换网络的图像情感分类[D].西南大学,2022.

[336] 许历隆.《第三章基于改进残差收缩网络的恶意应用细粒度分类方法》安卓恶意应用网络侧检测算法研究[D].南京信息工程大学,2022.

[337] 田科位.《第四章噪声环境下基于改进深度残差收缩网络的刀具磨损状态识别方法研究》基于深度学习的刀具磨损状态监测方法研究[D].重庆交通大学,2022.

[338] 闫新艳.《第三章基于深度残差收缩网络的商品图像识别》基于深度学习的商品识别技术研究[D].山西大学,2021.

[339] 熊志刚.《第3章基于深度残差收缩网络和注意力机制的非侵入式负荷分解》基于深度学习的家用电器非侵入式负荷分解方法研究[D].湘潭大学,2022.

[340] 王之卓.《3.4 基于深度残差收缩网络的LDPC译码方案》车载信道下基于深度学习的LDPC译码算法研究[D].浙江科技学院,2021.

[341] 陈甜甜.《第3章基于改进深度残差收缩网络的泄漏识别》基于深度学习的输油管道泄漏检测方法研究[D].**石油大学(北京),2023.

[342] 何芋钢.《5.3 基于加权融合改进深度残差收缩网络》&《5.4 基于MDC和加权DRSN的成型鼓故障诊断试验》基于数据增强和改进残差网络的轮胎成型鼓故障诊断研究[D].华南理工大学,2022.

[343] 王彦博.《基于深度残差收缩网络的电力系统频率安全集成评估》基于深度学习的电力系统扰动后频率安全评估[D].北京交通大学,2023.

[344] 刘相武.《第4章改进DRSN的往复式压缩机轴瓦故障诊断方法》基于改进残差网络的往复式压缩机轴瓦故障诊断方法研究[D].**石油大学(北京),2023.

[345] 胡从强.《5.2 基于注意力机制和深度残差收缩网络的电弧故障检测》基于多模态特征分析的低压串联电弧故障检测研究[D].沈阳航空航天大学,2023.

[346] 曲胤熹.《第3章一维深度残差收缩网络轴承故障诊断》基于深度学习的轴承预测性维护决策研究[D].沈阳大学,2023.

[347] 贾锐.《第三章基于深度残差收缩网络的回放攻击检测》基于Sinc滤波器的语音回放攻击检测研究[D].安徽大学,2023.

[348] 罗煜坤.《基于CWT-DRSN-SVM的轴承故障诊断》基于深度学习的滚动轴承故障诊断方法研究[D].盐城工学院,2024.

[349] 郭延昭.《第三章基于硬参数共享机制和深度残差收缩网络的无线感知算法》多任务学习框架下基于共享机制的无线感知技术研究[D].南京邮电大学,2023.

[350] 姚娜.《第3章基于自适应抗噪的DRSN的滚动轴承故障诊断》基于机器学习的风力机滚动轴承故障诊断[D].东北电力大学,2023.

[351] 康哲恺.《第3章基于深度残差收缩网络的恶意流量分类模型》基于深度学习的网络流量分类模型研究[D].中北大学,2023.

[352] 赵杰.《4.4 基于SET和DRSN的轴承智能故障诊断方法》基于时频特征提取的滚动轴承故障诊断方法研究[D].北京建筑大学,2022.

[353] 周晔.《第3章基于深度残差收缩网络的多特征联合检测模型》基于深度学习的语音欺骗检测[D].杭州电子科技大学,2023.

[354] 王梦琪.《第4章基于改进深度残差收缩网络的地震信号分类》基于改进深度残差网络的地震信号分类研究[D].广西师范大学,2022.

[355] 申平.《第四章基于MOMEDA和DRSN的滚动轴承故障模式分类》基于解卷积的滚动轴承故障特征提取与智能诊断算法研究[D].华东交通大学,2023.

[356] 麻建新.《5.3 基于深度残差收缩网络的故障诊断及其相关算法》基于电参时序分析的油井故障诊断方法[D].沈阳理工大学,2023.

[357] 于顼顼.《第3章基于深度残差收缩网络的城市环境声音识别算法》&《第4章基于改进的Peephole网络和深度残差收缩网络的城市环境声音识别算法》基于深度学习的城市环境声音识别算法研究及系统实现[D].桂林理工大学,2022.

[358] 裴雪武.《第四章基于改进深度残差收缩网络的滚动轴承寿命状态识别方法研究》滚动轴承早期失效判别及退化趋势预测方法研究[D].重庆交通大学,2022.

[359] 文培田.《第三章双通道输入的WGAN和DRSN的滚动轴承智能诊断》基于生成对抗网络的滚动轴承故障特征提取及智能识别[D].华东交通大学,2022.

[360] 陈飞.《第四章基于空洞残差收缩卷积网络的非侵入式负荷分解》面向住宅用户的非侵入式负荷监测方法研究[D].贵州大学,2022.

[361] 郭鲁豫.《第4章基于改进深度残差收缩网络的电力系统暂态稳定评估》基于机器学习的电力系统暂态稳定评估[D].华北电力大学,2021.

[362] 谢松林.《4.2.3 基于DRSN与LSTM的特征频段识别模型》基于多源信号的开关冗余电源健康评估研究[D].电子科技大学,2022.

[363] 郑国亮.《第3章基于深度残差收缩网络的线阵诊断方法》基于深度学习的阵列天线诊断方法研究[D].三峡大学,2022.

[364] 薛书鑫.《第3章基于残差收缩网络的关系抽取模型》基于深度学习的关系抽取研究[D].重庆邮电大学,2022.

[365] 童轶之.《第3章基于DRSN-LSTM模型的故障诊断》&《第4章基于DRSN-BILSTM模型的故障诊断》结合残差收缩和长短期记忆网络的轴承故障诊断[D].浙江理工大学,2022.

[366] 张谦.《4.3 基于深度残差收缩网络的分类算法》癌变胃黏膜的拉曼光谱分析及分类研究[D].兰州大学,2022.

[367] 陈世鹏.《第5章基于DRSN的改进TCN剩余寿命预测方法研究》船舶机械轴承故障识别与剩余寿命预测方法研究[D].江苏科技大学,2021.

[368] 阮智利.《2.2.2 基于DRSN自适应阈值冗余去除的双关节角度估计模型》肘腕关节连续运动估计及上肢外骨骼柔顺控制研究[D].武汉理工大学,2022.

[369] 高云飞.《4.1 融合残差收缩块的自注意力生成对抗网络进行人体姿态估计》基于自注意力生成对抗网络的人体姿态估计[D].**矿业大学,2021.

[370] 宋雨萱.《第二章基于深度残差收缩网络的鲁棒通信辐射源个体识别》演化深度学习研究与通信信号识别应用[D].西安电子科技大学,2020.

[371] 马鑫.《基于改进深度残差收缩网络的变压器故障特征识别》基于特征增强的变压器故障识别方法[D].华北水利水电大学,2021.

[372] 王然.《第4章基于深度密集残差收缩网络的图像超分辨率》基于改进卷积神经网络的图像超分辨率重建算法研究[D].湖北工业大学,2021.

[373] 倪天琦.《4.5 基于3D残差收缩网络的集合预报区域性订正模型》集合预报洋面阵风订正研究[D].天津大学,2020.

[374] 孟庆旭.《5.2 深度残差收缩网络的泄漏识别模型》带压气体管道泄漏超声波检测方法研究[D].南昌航空大学,2022.

[375] 李新玉.《第3章基于DRSN_CAM的动态主用户频谱感知算法》基于深度学习的频谱感知算法研究[D].杭州电子科技大学,2024.

[376] 宋育杰.《4.5 软判决下基于DRSN的LDPC码识别》基于深度学习的LDPC码闭集识别技术研究[D].西安电子科技大学,2023.

[377] 高怡宁.《特征聚类结合深度残差收缩的壁画风格迁移模型》墓室壁画风格迁移技术研究与应用[D].西安建筑科技大学,2023.

[378] 王彬宇.《基于深度残差收缩网络的电缆双故障监测》通信电缆故障智能监测技术研究[D].重庆大学,2022.

[379] 朱敬傲.《基于深度残差收缩网络的故障检测》基于机器学习的卫星姿态故障检测[D].**科学院大学(**科学院微小卫星创新研究院）,2022.

[380] 来庭煜.《基于优化S变换和改进深度残差收缩网络的识别方法》换流阀饱和电抗器铁心松动的振动及声纹识别研究[D].华北电力大学,2023.

[381] 邵梦园.《基于深度残差收缩网络的安卓恶意软件分类》基于深度主动学习的安卓恶意软件分类方法研究[D].西安电子科技大学,2022.

[382] 郑豪豪.《基于ResNet与DRSN的地磁数据去噪方法研究》基于参考道数据约束与深度学习的地磁数据去噪方法研究[D].东华理工大学,2023.

[383] 张毅恒.《第三章基于VMD和DRSN的IFF辐射源细微特征识别》智能计算在IFF中的关键技术研究及应用[D].江南大学,2023.

[384] 施娜.《第五章基于IDRSN的锂离子电池RUL预测》基于深度学习的锂离子电池健康状态评估和寿命预测研究[D].南京航空航天大学,2022.

[385] 李子睿.《基于自适应残差收缩网络的机械设备故障诊断方法》数据驱动的典型机械设备故障诊断技术研究[D].华中科技大学,2023.

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