Publication

Publications (2013-present)

  1. Masurkar F. and Tse P., ‘Analyzing the features of material nonlinearity evaluation in a rectangular Aluminum Beam using Rayleigh waves: Theoretical and Experimental study’, Journal of Physics Communications, 055002, 3(5), pp 1-23, 2019.
  2. Rostami J., Tse P.* and M. Yuen, ‘Detection of Broken Wires in Elevator Wire Ropes with Ultrasonic Guided Waves and Tone-Burst Wavelet’, Structural Health Monitoring, Manuscript ID SHM-18-0377.R2., Published June 12, 2019.
  3. Wan X., Zhang X.*, Fan H., Tse P., Dong M., Ma H., ‘Numerical Study on Ultrasonic Guided Waves for the Inspection of Polygonal Drill Pipes’, Sensors, 19(9), 2128; Published May 8, 2019.
  4. Tse Y., Cholette M., and Tse P., ‘A multi-sensor approach to remaining useful life estimation for a slurry pump’, Measurement, 139(2019) 140-151, March 15, 2019.
  5. Tse P.*, and Fang Z., ‘Novel Design of a Smart and Harmonized Flexible Printed Coil Sensor to Enhance the Ability to Detect Defects in Pipes’, NDT & E International, 103(2019), 48-61, April, 2019.
  6. Fang Z., and Tse P.*, ‘Demagnetization-based axial magnetized magnetostrictive patch transducers for locating defect in small-diameter pipes by using the non-axisymmetric guided wave’, Structural Health Monitoring, March 5, 2019.
  7. Wang Y., Tse P., Tang B., Qin Y., Deng L., Huang T., and Xu G., ‘Order spectrogram visualization for rolling bearing fault detection under speed variation conditions’, Mechanical Systems and Signal Processing, 122, 580–596, Jan. 2019.
  8. Xu F. and Tse P.*, ‘A method combined refined composite multiscale fuzzy entropy with PSO-SVM for roller bearings fault diagnosis’ (基于精细复合多尺度模糊熵与粒子群优化支持向量机的滚动轴承故障诊断), Journal of Central South University, Dec. 20, 2018.
  9. Xu F., Tse P. and Tse. Y., ‘Roller bearing fault diagnosis using stacked denoising autoencoder in deep learning and Gath–Geva clustering algorithm without principal component analysis and data label’, Applied Soft Computing, volume 73, pages 898-913, Dec. 2018.
  10. Tse P., ‘Novel Fault Diagnosis for Roller Bearing by using Multi Scale Sample Entropy based Clustering’, International Journal of Management and Applied Science (IJMAS)-IJMAS, 4(8), 1-6. August, 2018.
  11. Wang, G., Wei Y.H. and Tse P., ‘Clustering by defining and merging candidates of cluster centers via independence and affinity’, Neurocomputing, NEUCOM-D-18-00702R2, accepted August, 2018.
  12. Wang Y., Tse P., Tang B. Yi Qin, Deng L., Huang T. ‘Kurtogram manifold learning and its application to rolling bearing weak’, Measurement, 127, 533-545, Available online 18 June 2018.
  13. Fang Z., Tse P. and Wei Y., ‘Axial magnetized patch for efficient transduction of longitudinal guided wave and defect identification in concrete-covered pipe risers’, Structural Control and Health Monitoring, Accepted June 11, 2018.
  14. Masurkar F., and Tse P., Yelve N., ‘Investigating the critical aspects of evaluating the material nonlinearity in metal plates using Lamb waves: Theoretical and numerical approach’, Applied Acoustics, accepted June 19, 2018 in-press.
  15. Xu F. and Tse P., ‘Combined DBN in deep learning with AP clustering algorithm for roller bearings fault diagnosis without data label’, Journal of Vibration and Control, 12p., accepted 2018 5 28.
  16. Sun S. and Tse P., ‘Modeling of a horizontal asymmetric U-shaped vibration-based piezoelectric energy harvester (U-VPEH)’, Mechanical Systems and Signal Processing, 114 (2019) 467–485, accepted May 14, 2018, publish Jan. 2019.
  17. Xu F. and Tse P., ‘Automatic roller bearings fault diagnosis using DSAE in deep learning and CFS algorithm’, Soft Computing, pp. 1-12 April 2018.
  18. Wan X., Tse P, Xu G., and Zhang X. Xu G., Zhang, Q.; Fan, H.; Mao, Q.; Dong, M. and; Ma, H., ‘Numerical study on static component generation from the primary Lamb waves propagating in a plate with nonlinearity’, Smart Materials and Structures, 27, 4, 045006, April 2018.
  19. Masurkar F., and Tse P., Yelve N., ‘Evaluation of inherent and dislocation induced material nonlinearity in metallic plates using Lamb waves’, Applied Acoustics, 136 (Feb. 2018) 76–85.
  20. Yelve N., Tse P., Masurkar F., and ‘Theoretical and experimental evaluation of material nonlinearity in metal plates using Lamb waves’, Structural Control and Health Monitoring, 25, 6 e2164, June 2018.
  21. Tse P., Masurkar F., ‘Laser-based guided wave propagation and mode decomposition in detecting the integrity of structural I-beams’, Journal of Computer and Communications, 6(1), 42-55, Jan. 2018.
  22. Wang, G., Peter, W.T. and Yuan, M. Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector. Measurement Science and Technology29(2), 2018.
  23. Chen J, Tse P, Zhang H., ‘Integrated optical Mach-Zehnder interferometer-based defect detection using laser generated ultrasonic guided wave’ Optics Letters, Vol. 42, Issue 21, pp. 4255-4258, Nov. 1 2017
  24. Wan, X., Peter, W. T., Chen, J., Xu, G., & Zhang, Q. Second harmonic reflection and transmission from primary S0 mode Lamb wave interacting with a localized microscale damage in a plate: A numerical perspective. Ultrasonics82, 57-71, 2018.
  25. Chen, J., Rostami, J., Peter, W.T. and Wan, X. The design of a novel mother wavelet that is tailor-made for continuous wavelet transform in extracting defect-related features from reflected guided wave signals. Measurement110, pp.176-191, 2017.
  26. Rostami J., Tse P., and Fang Z. ‘Sparse and Dispersion-based Matching Pursuit for Minimizing the Dispersion Effect Occurred when Using Guided Wave for Pipe Inspection’, Materials.
  27. Rostami J., Chen J. and Tse P., ‘A Signal Processing Approach with Smooth Empirical Mode Decomposition to Reveal Hidden Trace of Corrosion in Highly Contaminated Guided Wave Signals for Concrete-Covered Pipes’, Sensors, 17(302).
  28. Zhong J., Tse P., and Wei Y., ‘An intelligent and improved density and distance-based clustering approach for industrial survey data classification’, Journal of Expert System with Application, 68, 2017.
  29. Sun S. and Tse P ‘Design and performance of a multimodal vibration-based energy harvester model for machine rotational frequencies’, Applied Physics Letters, 110, 243902 (2017).
  30. Zhong J., Tse P., and Wang D., ‘Novel Bayesian inference on optimal parameters of support vector machines and its application to industrial survey data classification’, Neurocomputing, 211, Oct. 26 2016.
  31. Sun S., Tse P, and Tse YL, ‘An enhanced factor analysis of performance degradation assessment on slurry pump impellers’, Shock and Vibrations, vol. 2017, Article ID 1524840, 13 pages, Jan. 2017. .
  32. Tse P., and Wang D., ‘State space formulation of nonlinear vibration responses collected from a dynamic rotor-bearing system: an extension of bearing diagnostics to bearing prognostics’, Sensors 2017, 17, 369, Feb. 14, 2017.
  33. Tse P. and Zhong J. ‘Smart Data Mining System for Automatically Assessing the Performance in Engineering Asset Management’, International Journal of Computer Science and Electronics Engineering (IJCSEE) 5(1), 2017.
  34. Wei Y.H., Tse P., Du B., and Wang Y., ‘An innovative fixed-pole numerical approximation for fractional order systems’, ISA Transactions, Elsevier Vol. 62, May 2016, pp.94-102.
  35. Wei Y.H., Tse P., S. Cheng, and Wang Y., ‘Adaptive backstepping output feedback control for a class of nonlinear fractional order systems", Nonlinear Dynamics, 86(2), 2016, 1047–1056. Published online: 27 July 2016.
  36. Tse P and Wang D., ‘Enhancing the abilities in assessing the slurry pumps’ performance degradation and estimating their remaining useful lives by using captured vibration signals’, Journal of Vibration and Control, Sept. 9, 2015.
  37. Wan X., Tse P, Xu G., Tao T. and Zhang Q. ‘Analytical and numerical studies of approximate phase velocity matching based nonlinear S0 mode Lamb waves for the detection of evenly distributed microstructural changes", Smart Materials and Structures, 25(4) (2016) 045023 (20pp), April 2016.
  38. Wan X., Wang D., Tse P., Xu G., and Zhang Q., ‘A critical study of different dimensionality reduction methods for gear crack degradation assessment under different operating conditions’, Measurement, Vol. 78, 138-150, 2016.
  39. Wan X., Xu G., Zhang Q., Tse P., and Tan H., ‘A quantitative method for evaluating numerical simulation accuracy of time-transient Lamb wave propagation with its applications to selecting appropriate element size and time step’, 64: 25-42, Ultrasonics, Jan, 2016.
  40. Wang D., Tsui K., Tse P., and Zuo M., ‘Principal Components of Superhigh-Dimensional Statistical Features and Support Vector Machine for Improving Identification Accuracies of Different Gear Crack Levels under Different Working Conditions’, Shock and Vibration, Vol. 2015, Article ID 420168, 14 pages, 2015.
  41. Wang D., W. Guo and Tse P., ‘An enhanced empirical mode decomposition method for blind component separation of a single-channel vibration signal mixture’, Journal of Vibration and Control, Vol. 22(11), June 2016, pp. 2603–2618.
  42. Esmaeili M., Oskouei A., Mirhadizadeh S., Tse P., and Hoshyar N. ‘Prediction of hydrodynamic bearing performance based on effective parameters by neural network’, International Journal of Engineering and Management Science, Vol. 7(2), April 15, 2016.
  43. Ng S., Cabrerab J., Tse P., Chen A., and Tsui K., ‘Distance-based Analysis of Dynamical Systems Reconstructed from Vibrations for Bearing Diagnostics’, Nonlinear Dynamics, Vol. 80(1-2), 147-165, April 2015.
  44. Zhang Q., Tse P., Ruana D. and Xua G., ‘Remaining Useful Life Estimation for Mechanical Systems Based on Similarity of Phase Space Trajectory’, Expert Systems with Application, 42 (2015) 2353–2360, on-line Nov. 4 2014.
  45. Wang D., and Tse P., ‘Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential Monte Carlo method’, Mechanical Systems and Signal Processing, vol. 56-57 (2015) 213-229.
  46. Wang D. Sun S. and Tse P., ‘A General Sequential Monte Carlo Method based Optimal Wavelet Filter: a Bayesian Approach for Extracting Bearing Fault Features’, Mechanical Systems and Signal Processing, vol. 52-53 (2015) 293-308, Accepted July 7, 2014. Online August, 2014.
  47. Tse, Y and Tse P., ‘A low-cost and effective automobile engine fault diagnosis by using instantaneous angular velocity evaluation’, Special Issue in International Journal of Strategic Engineering Asset Management (IJSEAM), July 25, 2014 Vol. 2, No.1, pp.2 -21.
  48. C. Shen, D. Wang, Y. Liu, g F. Kon, and Peter W. Tse. “Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines”, Smart Structures and Systems, 13(2014) pp. 453-471.
  49. S. Ng, Peter W. Tse and K. Tsui, ‘A One-Versus-All Class Binarization Strategy for Bearing Diagnostics of Concurrent Defects’, Sensors 14 (2014) pp. 1295-1321.
  50. C. Shen, F. Liu, D. Wang, A. Zhang, F. Kong and Peter W. Tse. ‘Doppler transient modeling based on Laplace wavelet and spectrum correlation assessment for locomotive bearing fault diagnosis’, Sensors, 13(2013) pp.15726-15746.
  51. J. Hu and Peter W. Tse, ‘A relevance vector machine-based approach with application to oil sand pump prognostics’, Sensors, 13(2013) pp.12663-12686.
  52. D. Wang, C. Shen and Peter W. Tse, ‘A novel adaptive wavelet stripping algorithm for extracting the transients caused by bearing localized faults’, Journal of Sound and Vibration, 332(2013), pp. 6871-6890.
  53. Y. Tse and Peter W. Tse, ‘A low-cost and effective automobile engine fault diagnosis by using instantaneous angular velocity evaluation’, Special Issue in International Journal of Strategic Engineering Asset Management, to appear.
  54. Chuan Li and Peter W. Tse, Fabrication and testing of an energy-harvesting hydraulic damper, Smart Materials and Structures, 22 (2013)  065024.
  55. Peter W. Tse and D. Wang, “The design of a new sparsogram for fast bearing fault diagnosis Part 1 of the two related manuscripts that have a joint title as “Two Automatic Vibration-based Fault Diagnostic Methods using the Novel Sparsity Measurement – Parts 1 and 2”, Mechanical Systems and Signal Processing, 40(2013) pp. 499-519.
  56. Peter W. Tse and D. Wang, “The automatic selection of an optimal wavelet filter and its enhancement by the new sparsogram for bearing fault detection Part 2 of the two related manuscripts that have a joint title as “Two Automatic Vibration-based Fault Diagnostic Methods using the Novel Sparsity Measurement – Parts 1 and 2”, Mechanical Systems and Signal Processing, 40 (2013), pp. 520-544.
  57. Peter W. Tse, Xiaojuan Wang, Characterization of pipeline defect in guided-waves based inspection through matching pursuit with the optimized dictionary, NDT & E International, 54 (2013) pp.171-182.
  58. C Shen, D Wang, F Kong, PW Tse, “Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier”, Measurement 46 (2013), pp. 1551-1564.
  59. Wei Guo, Peter W. Tse, A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals, Journal of Sound and Vibration, 332 (2013), pp.423-441.
  60. C. Shen, Q. He, F. Kong, and P. W. Tse, “A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis”, Journal of Mechanical Engineering Science, 227(2013), pp.1362–1370.
  61. Dong Wang, Peter W.Tse, Kwok Leung Tsui, "An enhanced Kurtogram method for fault diagnosis of rolling element bearings", Mechanical Systems and Signal Processing, 35(2013), pp.176–199.

 

Previously Selected Publications (before 2012)

1.

D. Wang, Peter W. Tse ,"A new blind fault component separation algorithm for a single-channel mechanical signal mixture", Journal of Sound and Vibration 331 (2012), pp.4956–4970.

2.

D. Wang, Peter W. Tse and Y. Tse, "A morphogram with the optimal selection of parameters used in morphological analysis for enhancing the ability in bearing fault diagnosis" Measurement Science and Technology 23 (2012) 065001 (15pp).

3.

W. Guo, Peter W. Tse , A. Djordjevich, "Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition" Measurement 45 (2012) pp. 1308-1322.

4.

F. Di Maio , J. Hu, Peter W. Tse, K. Tsui, E. Zio, and M. Pecht, ‘Ensemble-approaches for clustering health status of oil sand pumps”, Expert Systems with Applications, 39(5), Apr 2012, pp. 4847-4859.

5.

W. Guo, M. Hua, Peter W. Tse, and A. Mok, “Process Parameters Selection for Laser Polishing DF2 (AISI O1) by Nd:YAG Pulsed Laser Using Orthogonal Design”, International Journal of Advanced Manufacturing Technology, 59, Aug 2011, pp. 1009-1023.

6.

Peter W. Tse, X. Liu , Z. Liu, B. Wu, C. He, and X. Wang, “An Innovative Design of Using Flexible Printed Coil for Magnetostrictive-based Longitudinal Guided Wave Sensor in Steel Strand Inspection”, Smart Materials and Structures. 20 (2011) 055001, May 2011. (Featured article).

7.

X. Wang, Peter W. Tse and A. Dordjevich, "Evaluation of pipeline defect’s characteristic axial length via model-based parameter estimation in ultrasonic guided wave-based inspection" Measurement Science and Technology 22 (2011) 025701 (13pp).

8.

D. Wang, Peter W. Tse, W. Guo and Q. Miao, "Support vector data description for fusion
of multiple health indicators for enhancing gearbox fault diagnosis and prognosis" Measurement Science and Technology 22 (2011) 025102 (13pp).

9.

S. Savovic, A. Djordjevich, Peter W. Tse, and D. Krstic, “Radon diffusion in an anhydrous andesitic melt: a finite difference solution”, Journal of Environment Radioactivity, 102(2), 2011, Feb 2011, pp. 103-106.

10.

S. Savovic, Djordjevich A., Peter W. Tse, and D. Nikezic, “Explicit finite difference solution of the diffusion equation describing the flow of radon through soil”, Applied Radiation and Isotopes, 69(1), Jan 2011, pp. 237-240.

11.

S. Savovic , A. Djordjevich, Peter W. Tse, J. Zubia, J. Mateo, and M. Losada, “Determination of the width of the output angular power distribution in step-index multimode optical fibers” Journal of Optics. (Optics has been renamed to Journal of Optics A-pure and applied optics, 12(11), Nov 2010, pp. 1-5.

12.

A. Djordjevich, S. Savovic, Peter W. Tse, B. Drljaca, and A. Simovic, “Mode coupling in strained and unstrained step-index glass optical fibers”, Applied Optics, 49(27), Sep 2010, pp. 5076- 5080.

13.

X. Wang, Peter W. Tse, C. Mechefske and M. Hua, “Experimental investigation of the reflection in guided wave-based inspection for the characterization of pipeline defects”, the Journal of NDT&E International, Vol.43(4), 2010, pp.365-374.

14.

J. Rafiee, M. Rafiee, Peter W. Tse,, “Application of Mother Wavelet Functions for Automatic Gear and bearing Fault Diagnosis", Expert Systems with Applications, 37(6), Jun 2010, pp. 4568-4579.

15.

V. Sotiris, Peter W. Tse, and M. Pecht, “Anomaly detection through a bayesian support vector machine”, IEEE Transactions on Reliability, 59(2), Jun 2010, pp.277-286.

16.

Y. Li, Peter W. Tse , X. Yang and J. Yang, “EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine”, Mechanical Systems and Signal Processing, 24(1), Jan 2010, pp. 193-210.

17.

J. Rafiee and Peter W. Tse, “Use of autocorrelation of wavelet coefficients for fault diagnosis”, Mechanical Systems and Signal Processing, 23(5), Jul 2009, pp. 1554-1572. (The second most downloaded paper as of Sep 2009).

18.

C. Chan and Peter W. Tse, “A novel, fast, reliable data transmission algorithm for wireless machine health monitoring”, IEEE Transactions on Reliability (invited special section), 58, No. 2, Jun 2009,pp. 295-304.

19.

Peter W. Tse, and X. Wang, “Semi-quantitative analysis of defect in pipelines through the use of technique of ultrasonic guided waves”, Key Engineering Materials, 413-414, Special Volume – Damage Assessment of Structure VIII., May 2009, pp. 109-116. 

20.

J. Rafiee, Peter W. Tse, A.Harifi, and M. Sadeghi, “A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system", Expert Systems with Applications, 36(3P1), Apr 2009, pp.4862-4875.

21.

Y. Li, Peter W. Tse, and X. Wang, “Recovery of vibration signal based on a super-exponential algorithm”, Journal of Sound and Vibration, 311, Issues 1-2, Mar 2008, pp. 537-553.

22.

Peter W. Tse, S. Gontarz., and W. Wang, “Enhanced eigenvector algorithm for recovering multiple sources of vibration signals in machine fault diagnosis”, Mechanical Systems and Signal Processing, 21, No. 7, Oct 2007, pp. 2794-2813.

23.

W. Wang, Peter W. Tse, and J. Lee, “Remote machine maintenance system through internet and mobile communication”, International Journal of Advanced Manufacturing Technology, 31, No. 7-8, Jan 2007, pp. 783-789.

24.

Z. Peng, F. Chu, Peter W. Tse, “Singularity analysis of the vibration signals using wavelet modulus maxima method”, Mechanical Systems and Signal Processing, 21, Issue 2, Feb 2007, pp. 780-794.

25.

Peter W. Tse, J. Zhang, and X. Wang, “Blind-source-separation and blind equalization algorithms for mechanical signal separation and identification”, Journal of Vibration and Control, 12, Issue 4, Apr 2006, pp. 395-423.

26

Z. Peng, Peter W. Tse, and F. Chu, “An improved Hilbert - Huang transform and its application for vibration signals snalysis”, Journal of Sound and Vibration, 286, Aug 2005, pp. 187-205. (Awarded as one of the most read and cited articles in year 2006).

27

Z. Peng, Peter W. Tse, and F. Chu, “A comparison study of improved Hilbert-Huang transform and wavelet transform: application to fault diagnosis for rolling bearing”, Mechanical Systems and Signal Processing, 19(5), Sep 2005, pp. 974-988. (Ranked first of the top 25 articles in the review list with bibliography).

28.

W. Yang and Peter W. Tse, “An advanced strategy for detecting impulses in mechanical signals”, Journal of Vibration and Acoustics - Transactions of the ASME, 127 (3), Jun 2005, pp. 280-284.

29.

J. Wang, Peter W. Tse, L. He, R. Yeung, “Remote sensing, diagnosis and collaborative maintenance with web-enabled virtual instruments and mini-Servers”, International Journal of Advanced Manufacturing Technology, 24(9-10), Nov 2004, pp. 764-772.

30.

Peter W. Tse, W. Yang, and H. Tam, “Machine fault diagnosis through an effective exact wavelet analysis”, Journal of Sound and Vibration, 277(4-5), Nov 2004, pp.1005-1024.

31.

K. Ip, Peter W. Tse and H. Tam, “Extraction of patch-induced Lamb waves using a wavelet transform”, Smart Material and Structures, 13(4), Jun, 2004, pp.861-872 (ISSN 0964-1726).

32.

W. Lai, Peter W. Tse, G. Zhang, and T. Shi, “Classification of gear faults using cumulants and the radial basis function network”, Mechanical Systems and Signal Processing, 18(2), Mar 2004, pp. 381-389.