一種基于輕量級神經網絡的高鐵輪對軸承故障診斷方法

鄧飛躍; 丁浩; 呂浩洋; 郝如江; 劉永強

doi:10.13374/j.issn2095-9389.2020.12.09.001

一種基于輕量級神經網絡的高鐵輪對軸承故障診斷方法

doi: 10.13374/j.issn2095-9389.2020.12.09.001

鄧飛躍^{1, 2, ,},
丁浩²,
呂浩洋²,
郝如江²,
劉永強²

1.
石家莊鐵道大學省部共建交通工程結構力學行為與系統安全國家重點實驗室, 石家莊 050043
2.
石家莊鐵道大學機械工程學院, 石家莊, 050043

基金項目: 國家自然科學基金資助項目（11802184，11790282）；河北省自然科學基金資助項目（E2019210049）；河北省科技計劃項目資助項目（20310803D）；河北省“三三三人才工程”資助項目（A202101017）；北京市重點實驗室研究基金資助課題（PGU2020K009）

詳細信息

通訊作者:
E-mail:dengfy@stdu.edu.cn

中圖分類號: TG142.71
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- 文章訪問數: 2036
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- PDF下載量: 73
- 被引次數: 0
出版歷程
- 收稿日期: 2020-12-09
- 網絡出版日期: 2021-10-12
- 刊出日期: 2021-11-25

Fault diagnosis of high-speed train wheelset bearing based on a lightweight neural network

1.
China State Key Laboratory of Mechanical Behavior in Traffic Engineering Structure and System Safety, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
2.
School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

More Information

Corresponding author: E-mail:dengfy@stdu.edu.cn

摘要

摘要: 深度神經網絡技術用于機械設備故障診斷展現出了巨大潛力，但繁重復雜的計算量對計算機硬件提出了嚴苛的要求，嚴重限制了其在實際工程中的應用。基于此提出一種新型的輕量級神經網絡ShuffleNet，用于高速列車輪對軸承故障診斷研究。該網絡模型基于模塊化設計思想，包含多個高效率的ShuffleNet單元，通過運用分組卷積與深度可分離卷積技術極大改善了傳統卷積操作的運算效率；同時使用通道混洗方法克服了通道分組帶來的約束，改進了網絡的損失精度。實驗分析表明，所提網絡模型可有效用于復雜工況下高速列車輪對軸承故障診斷，相比傳統卷積神經網絡、殘差網絡和Xception等當前深度神經網絡模型，在保證診斷精度的同時，運行效率得到大幅提升。這為深度神經網絡技術應用于工程實際，克服計算機硬件條件限制提供了一條新的途徑。
- 輪對軸承 /
- 高速列車 /
- 故障診斷 /
- 深度神經網絡 /
- ShuffleNet單元
Abstract: Deep learning is gaining attention in the field of mechanical equipment fault diagnosis. With the help of deep learning techniques, deep neural networks (DNNs) have great potential for machinery fault diagnosis. Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to deliver state-of-the-art accuracy in various classifications of mechanical rotating parts. Convolutional neural networks (CNNs) are able to automatically learn multiple levels of representations from raw input datawithout introducing hand-coded rules or domain knowledge. Because of this powerful representation learning ability, deep learning has achieved great success in many fields. Although deep learning has achieved promising results in the field of machinery fault diagnosis, existing neural networks suffer from many limitations. The heavy and complex calculation amount puts forward strict requirements for computer hardware, which severely limits its application in actual engineering. To address this issue, this paper proposed a novel lightweight neural network model, ShuffleNet, for high-speed train wheelset bearing fault diagnosis. Based on the thought of module design, this model comprised several ShuffleNet units. Group convolution (GC) and deep separable convolution were used to improve the operation efficiency of traditional convolution in the ShuffleNet unit. Meanwhile, channel shuffle (CS) technology was adopted to overcome the grouping constraint caused by GC and improved the loss accuracy ofthenetwork model. CS operation makes it possible to build more powerful structures with multiple GC layers. Experimental results show that the proposed network model canbe applied in wheelset bearing fault diagnosis underacomplex working condition. Compared to the traditional CNN, ResNets, and Xception, the proposed method can greatly reducethecomputation cost while maintaining diagnosis accuracy. It is clear that the proposed lightweight neural network model, ShuffleNet, is superior to the above comparison models. This provides a new way forengineering applications of DNN technology and overcoming the limitations of computer hardware.
- wheelset bearing /
- high-speed train /
- fault diagnosis /
- deep neural network /
- ShuffleNet unit

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圖 1 分組卷積

Figure 1. Group convolution

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圖 2 標準卷積操作

Figure 2. Classical convolution operation

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圖 3 深度可分離卷積操作。（a）DWCnov操作；（b）PWConv操作

Figure 3. Depthwise separable convolutionoperation: (a) DWCnov operation; (b) PWConv operation

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圖 4 （a）通道孤立與（b）通道混洗的區別

Figure 4. Differencebetween (a) channel isolation and (b) channel shuffle

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圖 5 通道混洗操作

Figure 5. Channel shuffle operation

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圖 6 ShuffleNet單元結構。（a）ShuffleNet單元1; （b）ShuffleNet單元2

Figure 6. Architecture of ShuffleNetunit: (a) ShuffleNet unit 1; (b) ShuffleNet unit 2

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圖 7 ShuffleNet網絡模型結構

Figure 7. Architecture of ShuffleNet model

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圖 8 高速列車軸承綜合實驗臺

Figure 8. Wheelset bearing comprehensive test rig of high-speed train

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圖 9 故障輪對軸承。（a）內圈故障；（b）外圈故障

Figure 9. Wheelset bearing: (a) inner race fault; (b) outer race fault

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圖 10 所提網絡的分類準確度

Figure 10. Classification accuracy of the proposed network

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圖 11 不同方法的分類準確度

Figure 11. Classificationaccuracy of different methods

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圖 12 4個ShuffleNet單元網絡模型不同階段的可視化結果。（a）網絡輸入端；（b）卷積后；（c）首個ShuffleNet單元后；（d）第二個ShuffleNet單元后；（e）第三個ShuffleNet單元后；（f）第四個ShuffleNet單元后；（g）GAP后

Figure 12. Visualization results of the proposed network with 4 ShuffleNet units at different stages: (a) model input; (b) after convolution operation; (c) after the first ShuffleNet unit; (d) after the second ShuffleNet unit; (e) after the third ShuffleNet unit; (f)after the fourth ShuffleNet unit; (g) after the GAP

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圖 13 3個ShuffleNet單元網絡模型的不同階段可視化結果。（a）網絡輸入端；（b）卷積后；（c）首個ShuffleNet單元后；（d）第二個ShuffleNet單元后；（e）第三個ShuffleNet單元后；（f）GAP后

Figure 13. Visualization results of the proposed network with 3 ShuffleNet units at different stages: (a) model input; (b) after convolution operation; (c) after the first ShuffleNet unit; (d) after the second ShuffleNet unit; (e) after the third ShuffleNet unit; (f) after the GAP

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表 1 網絡參數設置

Table 1. Parameter settings of network

Sequencenumber	Structure name	Parameter values		Output
1	Standard convolution layer	Conv(3,3,1,64)		32×32
2	ShuffleNet unit1		GConv(1,1,1,64)	32×32
			DWConv(3,3,1,64)
			GConv(1,1,1,64)
3	ShuffleNet unit2	AVG pool (3,3,2,64)	GConv(1,1,1,64)	16×16
			DWConv(3,3,2,64)
			GConv(1,1,1,64)
4	ShuffleNet unit1		GConv(1,1,1,128)	16×16
			DWConv(3,3,1,128)
			GConv(1,1,1,128)
5	ShuffleNet unit2	AVG pool (3,3,2,128)	GConv(1,1,1,128)	8×8
			DWConv(3,3,2,128)
			GConv(1,1,1,128)
6	GAP			256×1
7	Dropout			256×1
8	Fully connected output layer			3

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表 2 實驗數據

Table 2. Experimental data

Sequence number	Rotational speed/ (r·min^?1)	Speed/ (km·h^?1)	Load	Inner race fault	Outer race fault	Normal
C1	1200	200	No	1000	1000	1000
C2			Static	1000	1000	1000
C3			Dynamic	1000	1000	1000
C4	1500	250	No	1000	1000	1000
C5			Static	1000	1000	1000
C6			Dynamic	1000	1000	1000
C7	1800	300	No	1000	1000	1000
C8			Static	1000	1000	1000
C9			Dynamic	1000	1000	1000
C10	2100	350	No	1000	1000	1000
C11			Static	1000	1000	1000
C12			Dynamic	1000	1000	1000

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表 3 不同分組數的對比結果

Table 3. Comparison result of different groups

Group number	Test accuracy/ %	Time/s		Number of model parameters	Flops
Group number	Test accuracy/ %	Training	Testing	Number of model parameters	Flops
G=2	99.99	10146	10.87	50627	30991874
G=4	99.99	10266	11.33	30147	17360386
G=8	99.99	10465	11.83	20035	10806786

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表 4 不同方法的運行效率對比

Table 4. Comparison of operation efficiency of different methods

Model	Time/s		Number of model parameters	Flops
Model	Training	Testing	Number of model parameters	Flops
Proposed model	10232	11.14	50627	30991874
CNN	13363	12.36	668675	567444224
ResNets	14726	15.44	677251	571835136
Xception	14310	13.66	92739	115999488

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