ljmp论文审稿意见 - 副本

1、Reviewer comments as below:#Reviewer 1Comments for JPEM-D-13-00684In this paper, the author developed a novel method based on a morphological filter and signal complexity measure from a new eddy-current sensor. The results show that the proposed method is effective for detecting quality problems wit

2、h roller bearings, showing the high sensitivity in resolve weak signals. It may be extended to the problems of signal processing in acceleration based method. However, more efforts should be given to make comments on the method used to support the methods in use. In addition please improve the Engli

3、sh to reduce typing and grammar errors.Also please address the concerns bellow AbstractComment 1: Change the Different from signals in the process into Unlike signals .The authors Answer: Thanks for the reviewers suggestion. Corrected accordingly.Comment 2: Please consider the following suggestion f

4、or the abstractBearing defectivepection plays a vital role in bearing quality control. Unlike signals in theprocess of condition monitoring and fault diagnosis, the signal characteristic of defective bearingsis much weaker and difficult to be quantified through the acceleration based techniques. In

5、thispaper, a novel system is developed topect automatically the small defects of roller bearings foron-line quality control. Rather than using acceleration based techniques the system employs a high sensitive eddy current sensor to measure the displacement profiles of the outer race for high signal

6、to noise ratio. Furthermore, a morphological filter is used to enhance the feature signal which is subsequently measured by Kolmogorov complexity measure. Both simulated signals and measured data show that this system is able to diagnose defects including abnormal surface roundness, waviness, misali

7、gned races which are typical quality problems in bearing manufacturing lines.The authors Answer: Thanks a lot for the reviewers suggestion. Corrected accordingly.IntroductionComment 3: In the first paragraph in introduction, in line 4, pection measures can beclassified into two steps to avoid defect

8、s, should be avoid; in line 8, caused by manufacturing error or abrastive wear, should be abrasive.The authors Answer: Thanks for the reviewers suggestion. Corrected accordingly.Comment 4: In the first paragraph of section 2.1, line 3 and line 5, and section 2.2, line 8, elestic should be elastic.Th

9、e authors Answer: Thanks for the reviewers suggestion. Corrected accordingly.Comment 5: Revise the first sentence of the third paragraph.The authors Answer:The sentence has been changed as follow: Unlike signals in the process of condition monitoring and fault diagnosis2,3,4,5, the signal characteri

10、stic of defective bearings is quite weak.Comment 6: Corresponding previous works using new signal processing methods should be reviewed.The authors Answer: Thanks to the reviewers advice, we have joined the following sentences and references in the introduction.Part 1:A series of method of the extra

11、ction of weak signal has been brought out, such as the stochastic resonance, the Morlet wavelet, the cyclic Wiener filter and envelope spectrum6,7,8,9. The above method is restricted in the execution efficiency and the success rate of code in the online measurement. In this paper, authors are aimed

12、at getting the high signal-to-noise (SNR) signal from the sensor to reduce the difficulty of the late signal processing.6. Siliang, Lu., Qingbo, He., Fanrang, Kong., “Stochastic resonance with WoodsSaxon potential for rolling element bearing fault diagnosis,” Mechanical Systems and Signal Processing

13、., Vol.45, pp.488-503, 2014.7. Yang Ming, Jin Chen., Guangming, Dong., “Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum,” Mechanical Systems and Signal Processing., Vol.25, pp.1773-1785, 2011.8. Yaguo, Lei., Jing, Lin., Zhengjia, He., “Application

14、 of an improved kurtogram method for fault diagnosis of rolling element bearings,” Mechanical Systems and Signal Processing., Vol.25, pp.1738-1749, 2011.9. Haiyang, Liu., Weiguo, Huang., Shibin, Wang., Zhongkui, Zhu., “Adaptive spectral kurtosis filtering based on Morlet wavelet and its application

15、for signal transients detection,” MechanicalSystems and Signal Processing., 2014.Part 2:As an index in the time domain, the Kolmogorov has been found to be an effective tool for signal analysis and condition assessment in a bearing system11. The Kolmogorov can be used to extract characteristics whic

16、h are then used to evaluate bearing quality and to trace to sources including roundness, roughness, waviness and other indicators of quality defects.11. Ruqiang, Yan., “Complexity as a Measure for Machine Health Evaluation,” IEEE Transactionson IAM, Vol. 55, pp.1327-1334, 2004.Part 3:Morphology filt

17、er is an efficient signal processing tool in the time domain12.12. Jing, Wang., Guanghua, Xu., “Application of improved morphological filter to the extraction of impulsive attenuation signals,” Mechanical Systems and Signal Processing., Vol.23, pp.236-245, 2009.Theoretical BackgroundComment 7: Secti

18、on 2.1 and section 2.2 have the same title.The authors Answer: Thanks to the reviewers advice, we have changed the title of section 2.2 as “Measuring bearing defects by means of the morphological filter”Comment 8: Fig. 1(a) shows., rather than describes. In addition, the figure quality should be imp

19、roved.The authors Answer: Thanks for the reviewers suggestion. Corrected accordingly.Comment 9: In the first paragraph of section 2.1, line 3 and line 5, and section 2.2, line 8, elestic should be elastic.The authors Answer: Thanks for the reviewers suggestion. Corrected accordingly.Comment 10: Ln 4

20、2, p2, changing while While into However will make more sense.The authors Answer: Thanks for the reviewers suggestion. Corrected accordingly.Comment 11: In the third paragraph of section 2.2, In this paper, we utilize an average weighted combination of open-closing and close-opening operation, pleas

21、e explain why choose this method.The authors Answer: The opening operation can smooth the signal from below by cutting down its peaks, and the closing operation can smooth the signal from above by filling up its valleys. As shown in Fig. 7, the displacement sensor signal is close to the symmetrical

22、shape after the operation of removing mean. Thus we utilize an average weighted combination of open-closingand close-opening operation in this paper.Fig. 7. Signals of five types of bearings a) Quality qualified bearings. b) Abnormal roughness onouter raceway. c) Abnormal roughness on inner raceway.

23、 d) Bruise on outer raceway. e) Bruise on inner raceway.Comment 12:ection 2.3, please add more explanation about the complexity measurealgorithm to make it clearer.The authors Answer:Comment 13: From the graphs, it looks that the main trend of signals can be removed by a conventional low pass filter

24、 which is efficient and reliable. What is the key benefit for using the morphological filter ?The authors Answer:Fig. 9. Results of morphological filter a) The original signal. b) The periodic signal. c) The impact signal.1. With the problem of the same frequency interference.As shown in Fig.9, the

25、impulsive signals which are intensity related to the quality of bearings have the same frequency with the main trend of signals. The low pass filter may filter useful compositions of signals.2. Morphological filter can help to identify different quality problems.As shown in Fig.7, signals of bearing

26、s with abnormal roughness trend to cosine waveform classes, while signals of bearings with bruise problems trend to triangle waveform classes. Morphological filter can help to identify different quality problems to some extent.3. Morphological filter has high code execution efficiency. The running t

27、ime is about 0.36s using aThinkpad T410i computer, which can be used for on-line or off-line monitoring.Comment 14: Line 18, P3 to characterize rather than characterized.The authors Answer: Thanks for the reviewers suggestion. Corrected accordingly.Comment 15: Line17, References are required for the

28、 key facts described this paragraph.The authors Answer:原参考文献 8（12）和参考文献 9（13）Comment 16: How the digitalized signal can be manipulated as strings for calculating the Kolmogorov complexity values. More references or description required.The authors Answer:找合适的参考文献即可.The Kolmogorov complexity has been

29、 found to be an effective tool for signal analysis and condition assessment in a bearing system.As an index in the time domain, the Kolmogorov has been found to be an effective tool for signal analysis and condition assessment in a bearing system11. The Kolmogorov can be used to extract characterist

30、ics which are then used to evaluate bearing quality and to trace to sources including roundness, roughness, waviness and other indicators of quality defects.11. Ruqiang, Yan., “Complexity as a Measure for Machine Health Evaluation,” IEEE Transactionson IAM, Vol. 55, pp.1327-1334, 2004.SimulationComm

31、ent 17: What is the type of noise in the simulated signals?The authors Answer:1. The complexity of the signal is closely related to the composition of signal. The noisedetermines the complexity of the signal in the simulation. The used noise is colored noise,containing different spectrum structures.

32、 As for the white noise, regardless of its intensity change, the result is verified its complexity is essentially the same.Fig. 3 Simulations of the complexity2. In the original manuscript, the author provides results of the simulation signal and the complexity as Fig. 3.The noise used come from an

33、actual run-to-failure test measured by an acceleration sensor 网 址 .After reviewing and carefully analysis the opinion of the reviewer, authors think that the picture is not as good as to describe the complexity of the signal and it may confuse readers. Therefore, we use another figure in this paper

34、as below.Those data sets are constructed with different typical signals, such as sinusoidal, sinusoidal with amplitude modulation, sinusoidal with frequency modulation, and white noise, and they are used to test theverification of complexity.(要加到论文中)Fig. 3. Lempel-Ziv index values of different simul

35、ation of signalsThanks for the reviewers valuable suggestion which has made an important guidance.Comment 18: The title of Fig. 3 is not correct, please check.The authors Answer: Thanks for the reviewers suggestion. The title of Fig. 3 has been changed with “Simulations of the complexity”.Experiment

36、al.Comment 19: More detail should be provided for the sensor. Especially how difference from a normal eddy current sensor that makes it more sensitive and accurate.The authors Answer: Thanks for the reviewers suggestion.1. The elastic deformation is quite small, and the value is in the range of 0.1

37、to 20 microns.2. Due to the vibration quantity is very small; there are almost no output signals from acceleration sensor. Thank you very much for the referees precious opinion and our team is ready to buy an acceleration sensor from NSK for the next experiment3. The eddy current sensor is used to d

38、etect the tiny deformation of outer ring. Compared with acceleration sensor, this method is more sensitive and accurate. However, the common eddy current sensors can achieve the corresponding detection results as long as the detection range is within the range of 0.1 to 20 microns. Im very sorry bec

39、ause of the authors inappropriate describebring the confusion to the reviewer.Comment 20: A comparative result should be provided to convince the proposed method is more effective.The authors Answer: Thanks for the reviewers suggestion.There are quite a number of papers about the fault detection of

40、bearings, while the research of problems of bearing quality is less. The main testing method is through the detection of the static geometry size of each component of bearings. We also try to use the vibration acceleration sensor and the acoustic emission sensor; results show that the vibration quan

41、tity is very small; there are almost no output signals from acceleration sensor, which cannot be used for the detection of the problem of the bearing quality. It is found that the acoustic emission sensor can be implemented to detect the lubrication state of bearings, while it can also not be used f

42、or the detection of theproblem of the bearing quality.The authors would like to thank the reviewer for their suggestions that help to improve the quality of this work.ightful comments and usefulReviewer #2: This paper mainly discusses bearing quality evaluation based on morphology filterand the kolm

43、ogorov complexity. This paper is somewhat interesting, but it need to be further improved. The comments are given below.Comment 1: In introduction, the main bearing defects evaluation methods in time domain arecomplexity which are published in this journal or other journals, should be detailed in th

44、e first section.The authors Answer: Thanks to the reviewers advice, we have joined the following sentences and references in the introduction.Part 1:A series of method of the extraction of weak signal has been brought out, such as the stochastic resonance, the Morlet wavelet, the cyclic Wiener filte

45、r and envelope spectrum6,7,8,9. The above method is restricted in the execution efficiency and the success rate of code in the online measurement. In this paper, authors are aimed at getting the high signal-to-noise (SNR) signal from the sensor to reduce the difficulty of the late signal processing.

46、6. Siliang, Lu., Qingbo, He., Fanrang, Kong., “Stochastic resonance with WoodsSaxon potential for rolling element bearing fault diagnosis,” Mechanical Systems and Signal Processing., Vol.45, pp.488-503, 2014.7. Yang Ming, Jin Chen., Guangming, Dong., “Weak fault feature extraction of rolling bearing

47、 based on cyclic Wiener filter and envelope spectrum,” Mechanical Systems and Signal Processing., Vol.25, pp.1773-1785, 2011.8. Yaguo, Lei., Jing, Lin., Zhengjia, He., “Application of an improved kurtogram method for fault diagnosis of rolling element bearings,” Mechanical Systems and Signal Process

48、ing., Vol.25, pp.1738-1749, 2011.9. Haiyang, Liu., Weiguo, Huang., Shibin, Wang., Zhongkui, Zhu., “Adaptive spectral kurtosis filtering based on Morlet wavelet and its application for signal transients detection,” MechanicalSystems and Signal Processing., 2014.Part 2:As an index in the time domain,

49、the Kolmogorov has been found to be an effective tool for signal analysis and condition assessment in a bearing system11. The Kolmogorov can be used to extract characteristics which are then used to evaluate bearing quality and to trace to sources including roundness, roughness, waviness and other i

50、ndicators of quality defects.11. Ruqiang, Yan., “Complexity as a Measure for Machine Health Evaluation,” IEEE Transactionson IAM, Vol. 55, pp.1327-1334, 2004.Part 3:Morphology filter is an efficient signal processing tool in the time domain12.12. Jing, Wang., Guanghua, Xu., “Application of improved

51、morphological filter to the extraction of impulsive attenuation signals,” Mechanical Systems and Signal Processing., Vol.23, pp.236-245, 2009.Comment 2: Table 1 and Table 2 are very important data to verify the method proposed in this paper. But it is not clear whether enough or batch bearings are u

52、sed to obtain the result in these two tables. How many bearings used should be explained clearly as now it is very vague.The authors Answer: Thanks to the reviewers precious remind, and we have changed the manuscript as follow:Due to the support of the project, test bearings in this paper are 8306 made by the LYC Company in Chin