Sage Journals: Discover world-class research

Abstract

Failure time prognosis in manufacturing process plays a crucial role in guaranteeing manufacturing safety and reducing maintenance loss. However, most current prognosis methods face great difficulty when handling massive data collected from manufacturing process. Convolutional neural network (CNN) provides an effective way to extract features with massive data. Due to the difference between images and multisensory signals, CNN is not suitable for machining process. Inspired by the idea of CNN, a novel prognosis framework is proposed based on the characteristics of multisensory signals, which is called multi-dislocated time series convolutional neural network (MDTSCNN). The proposed MDTSCNN is composed of multi-dislocate layer, convolutional layer, pooling layer and fully connected layer. By adding a multi-dislocate layer, this model can learn the relationship between different signals and different intervals in periodic multisensory signals. The effectiveness of proposed method is validated by a milling process. Compared to other prognosis method, the proposed MDTSCNN shows enhanced performances in prediction accuracy.

Keywords

Failure time prognosis multisensory sequential signals multi-dislocated layer multi-dislocated time series convolutional neural network

Get full access to this article

View all access options for this article.

References

Teti

Jemielniak

O’Donnell

, et al. Advanced monitoring of machining operations. Cirp Ann-Manuf Techn 2010; 59: 717–739.

Cheng

Zhu

, et al. Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks. IEEE Trans Ind Inform 2019; 15: 987–997.

Hanachi

Liu

Banerjee

, et al. A physics-based modeling approach for performance monitoring in gas turbine engines. IEEE Trans Reliab 2015; 64: 197–205.

Ding

Sun

JQ.

Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab Eng Syst Saf 2018; 172: 1–11.

Zhou

Chang

Tian

, et al. A reliability optimization allocation method for the hydraulic system of CNC honing machine considering the operating condition information. Proc IMechE, Part C: J Mechanical Engineering Science 2018; 232: 2244–2264.

Zhang

Zhou

Yang

, et al. View-based 3-d cad model retrieval with deep residual networks. IEEE Trans Ind Inform 2020; 16: 2335–2345.

Chen

WH.

Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Trans Instrum Measure 2017; 66: 1693–1702.

Kuo

Y-H

Kusiak

From data to big data in production research: the past and future trends. Int J Prod Res 2018; 57: 1–26.

Cheng

, et al. Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system. Appl Soft Comput 2018; 68: 13–23.

10.

Chang

Zhou

Zhang

, et al. A service-oriented dynamic multi-level maintenance grouping strategy based on prediction information of multi-component systems. J Manuf Syst 2019; 53: 49–61.

11.

Putz

Wiener

Pierer

, et al. A multi-sensor approach for failure identification during production enabled by parallel data monitoring. Cirp Ann-Manuf Techn 2018; 67: 491–494.

12.

Atamuradov

Medjaher

Lamoureux

, et al. Fault detection by segment evaluation based on inferential statistics for asset monitoring. In: Annual conference of the Prognostics and Health management Society. Florida, United States, 2017, pp. 58–67.

13.

Kannatey-Asibu

Yum

Kim

TH.

Monitoring tool wear using classifier fusion. Mech Syst Signal Process 2017; 85: 651–661.

14.

Torabi

, et al. Application of clustering methods for online tool condition monitoring and fault diagnosis in high-speed milling processes. IEEE Syst J 2016; 10: 721–732.

15.

Pang

Zhou

J-H

Yan

H-C.

Pdf and breakdown time prediction for unobservable wear using enhanced particle filters in precognitive maintenance. IEEE Trans Instrumen Measure 2015; 64: 649–659.

16.

Zhu

Liu

TS.

Online tool wear monitoring via hidden semi-Markov model with dependent durations. IEEE Trans Ind Inform 2018; 14: 69–78.

17.

Zhu

Lin

Tool condition monitoring with multiscale discriminant sparse decomposition. IEEE Trans Ind Inform 2019; 15: 2819–2827.

18.

Kusiak

Smart manufacturing must embrace big data. Nature 2017; 544: 23–25.

19.

Abdel-Hamid

Mohamed

Jiang

, et al. Convolutional neural networks for speech recognition. IEEE-ACM Trans Audio Speech Language Process 2014; 22: 1533–1545.

20.

Krizhevsky

Sutskever

Hinton

GE.

Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS), MIT Press, Cambridge, MA, 2012, pp. 1097–1105.

21.

Szegedy

Liu

Jia

, et al. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Piscataway: IEEE, 2015, pp.1–9.

22.

Janssens

Slavkovikj

Vervisch

, et al. Convolutional neural network based fault detection for rotating machinery. J Sound Vibr 2016; 377: 331–345.

23.

Weimer

Scholz-Reiter

Shpitalni

Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. Cirp Ann-Manuf Techn 2016; 65: 417–420.

24.

Zhu

Chen

Peng

WW.

Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Trans Ind Electr 2019; 66: 3208–3216.

25.

Zhu

Peng

Chen

, et al. A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis. Neurocomputing 2019; 323: 62–75.

26.

Liu

Meng

Yang

, et al. Dislocated time series convolutional neural architecture: an intelligent fault diagnosis approach for electric machine. IEEE Trans Ind Inform 2017; 13: 1310–1320.

27.

Wang

Chen

, et al. Mixed pooling for convolutional neural networks. In: International conference on rough sets and knowledge technology. Berlin: Springer, Cham, 2014, pp. 364–375.

28.

Glorot Bengio

XY.

Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, Sardinia, Italy, 2010, pp. 249–256.

29.

Zeng

Thoe

, et al. Multi-modal sensing for machine health monitoring in high speed machining. In: 2006 4th IEEE international conference on industrial informatics. Piscataway: IEEE, 2006, pp.1217–1222.

30.

Lim

Zhou

, et al. Fuzzy neural network modelling for tool wear estimation in dry milling operation. In: Annual conference of the prognostics and health management society. San Diego, CA, United States, 2009, pp. 1–11.

31.

Benkedjouh

Medjaher

Zerhouni

, et al. Health assessment and life prediction of cutting tools based on support vector regression. J Intell Manuf 2015; 26: 213–223.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.01 MB

Failure time prognosis in manufacturing process using multi-dislocated time series convolutional neural network

Abstract

Keywords

Get full access to this article

References

Supplementary Material