Acoustic signals play an essential role in machine state monitoring. Efficient processing of real-time machine acoustic signals improves production quality. However, generating semantically useful information from sound signals is an ill-defined problem that exhibits a highly non-linear relationship between sound and subjective perceptions. This paper outlines two neural network models to analyze and classify acoustic signals emanating from machines:(i) a backpropagation neural network (BPNN); and (ii) a convolutional neural network (CNN). Microphones are used to collect acoustic data for training models from a computer numeric control (CNC) lathe. Numerical experiments demonstrate that CNN performs better than the BP-NN.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-019-00254-5
Ruo-Yu Yang
,
Rahul Rai
. Machine auscultation: enabling machine diagnostics using convolutional neural networks and large-scale machine audio data[J]. Advances in Manufacturing, 2019
, 7(2)
: 174
-187
.
DOI: 10.1007/s40436-019-00254-5
1. Huang HB, Huang XR, Li RX et al (2016) Sound quality prediction of vehicle interior noise using deep belief networks. Appl Acoust 113:149-161
2. Sharan RV, Moir TJ (2016) An overview of applications and advancements in automatic sound recognition. Neurocomputing 200:22-34
3. Kumon M, Yoshihiro ITO, Nakashima T et al (2007) Sound source classification using support vector machine. IFAC Proc Vol 40(13):465-470
4. Thaler T, Potočnik P, Bric I et al (2014) Chatter detection in band sawing based on discriminant analysis of sound features. Appl Acoust 77:114-121
5. Dombovari Z, Barton DAW, Wilson RE et al (2011) On the global dynamics of chatter in the orthogonal cuttingmodel. Int J Non-lin Mech 46(1):330-338
6. Pan G, Xu H, Kwan CM et al (1996) Modeling and intellligent chatter control strategies for a lathe machine. Control Eng Pract 4(12):1647-1658
7. https://github.com/ruoyuyang1991/machine-auscultation-classification.Accessed 10 April 2019
8. Singh KK, Singh R, Kartik V (2015) Comparative study of chatter detection methods for high-speed micromilling of Ti6Al4V. Proc Manufacturing 1(1):593-606
9. Li XQ, Wong YS, Nee AYC (1997) Tool wear and chatter detection using the coherence function of two crossed accelerations. Int J Mach Tools Manuf 37(4):425-435
10. Kuljanic E, Sortino M, Totis G (2008) Multisensor approaches for chatter detection in milling. J Sound Vib 312(4-5):672-693
11. Toh CK (2004) Vibration analysis in high speed rough and finish milling hardened steel. J Sound Vib 278(1):101-115
12. Soliman E, Ismail F (1997) Chatter detection by monitoring spindle drive current. Int J Adv Manuf Technol 13(1):27-34
13. Delio T, Tlusty J, Smith S (1992) Use of audio signals for chatter detection and control. J Manuf Sci Eng 114(2):146-157
14. Li XQ, Wong YS, Nee AYC (1988) A comprehensive identification of tool failure and chatter using a parallel multi-art2 neural network. J Manuf Sci Eng 120(2):433-442
15. Jiang AY, Zhang C (2006) Hybrid HMM/SVM method for predicting cutting chatter. In:Proceedings of SPIE-the international society for optical engineering 6280:62801Q-8
16. Yao ZH, Mei DQ, Chen ZC (2010) On-line chatter detection and identification based on wavelet and support vector machine. J Mater Process Technol 210(5):713-719
17. Bediaga I, Munoa J, Hernández J et al (2009) An automatic spindle speed selection strategy to obtain stability in high-speed milling. Int J Mach Tools Manuf 49(5):384-394
18. Zhang CL, Yue X, Jiang YT (2010) A hybrid approach of ann and hmm for cutting chatter monitoring. Adv Mater Res 97:3225-3232
19. Cao HR, Lei YG, He ZJ (2013) Chatter identification in end milling process using wavelet packets and hilbert-huang transform. Int J Mach Tools Manuf 69:11-19
20. Kondo E, Ota H, Kawai T (1997) A new method to detect regenerative chatter using spectral analysis, part 1:Basic study on criteria for detection of chatter. J Manuf Sci Eng 119(4A):461-466
21. Tansel IN, Wang X, Chen P et al (2006) Transformations in machining. Part 2:evaluation of machining quality and detection of chatter in turning by using s-transformation. Int J Mach Tools Manuf 46(1):43-50
22. Cho DW, Eman KF (1988) Pattern recognition for on-line chatter detection. Mech Syst Signal Process 2(3):279-290
23. Grabec I, Gradišek J, Govekar E (1999) A new method for chatter detection in turning. CIRP Ann Manuf Technol 48(1):29-32
24. Berger B, Belai C, Anand D (2003) Chatter identification with mutual information. J Sound Vib 267(1):178-186
25. Choi T, Shin YC (2003) On-line chatter detection using waveletbased parameter estimation. Trans-Am Soc Mech Eng J Manuf Sci Eng 125(1):21-28
26. Lécun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278-2324
27. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 1:1097-1105
28. Simonyan K, Zisserman A (2014) Very deep convo-lutional networks for large-scale image recognition. arXiv preprint arXiv:1409-1556
29. Szegedy C, Liu W, Jia YQ er al (2015) Going deeper with convolutions. In:Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1-9
30. Girshick R, Donahue J, Darrell T (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In:Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580-587
31. Girshick R (2015) Fast R-CNN. In:Proceedings of the IEEE international conference on computer vision, pp 1440-1448
32. Ren SQ, He KM, Girshick R et al (2015) Faster R-CNN:Towards real-time object detection with region proposal networks. Adv Neural Inform Process Syst 1:91-99
33. Lécun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436-444
34. Piczak KJ (2015) Environmental sound classification with convolutional neural networks. In:IEEE 25th International workshop on machine learning for signal processing (MLSP), 2015, pp 1-6
35. Boddapatia V, Petef A, Rasmusson J et al (2017) Classifying environmental sounds using image recognition networks. Proc Comput Sci 112:2048-2056
36. Fu Y, Zhang Y, Gao Y et al (2017) Machining vibration states monitoring based on image representation using convolutional neural networks. Eng Appl Artif Intell 65:240-251
37. Loh WL (1996) On latin hypercube sampling. Ann Stat 24(5):2058-2080
38. Vaseghi SV (2008) Advanced digital signal processing and noise reduction. Wiley, New York
39. Gupta CN, Palaniappan R, Swaminathan S et al (2007) Neural network classification of homomorphic segmented heart sounds. Appl Soft Comput 7(1):286-297
40. Kotani M, Katsura M, Ozawa S (2004) Detection of gas leakage sound using modular neural networks for unknown environments. Neurocomputing 62:427-440
41. Potočnik P, Thaler T, Govekar E (2013) Multisensory chatter detection in band sawing. Proc CIRP 8:469-474
42. Liang HY, Nartimo I (1998) A feature extraction algorithm based on wavelet packet decomposition for heart sound signals. In:Proceedings of the IEEE-SP international symposium, pp 93-96
43. Babaei S, Geranmayeh A (2009) Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of pcg signals. Comput Biol Med 39(1):8-15
44. O'shaughnessy D (1987) Speech communication:human and machine. Universities Press, Hyderabad
45. Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. Technical report, University of Toronto 1(4):7
46. Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout:A simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929-1958
