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Integrating advanced measurement and signal processing for reliability decision-making
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Lublin University of Technology, Faculty of Management, ul. Nadbystrzycka 38, 20-618 Lublin, Poland
Rzeszow University of Technology, Faculty of Mechanical Engineering and Aeronautics, ul. Powstańców Warszawy 8, 35-959, Rzeszów, Poland
Lublin University of Technology, Mechanical Engineering Faculty, ul. Nadbystrzycka 36, 20-618 Lublin, Poland
Rzeszow University of Technology, Faculty of Electrical and Computer Engineering, ul. W. Pola 2, 35-959 Rzeszów, Poland
Publication date: 2021-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(4):777-787
  • Force and torque sensors analysed as an alternative to the vibration measurement.
  • Effective condition prediction when integrated with adequate signal processing.
  • Decision trees with various types of wavelets selected for predictive models.
  • High accuracy method proposed to trace tool condition in real-time.
An advanced milling machine multi-sensor measurement system as a condition monitoring tool was presented. It was assumed that the data collected from the 3-axis force and torque sensor can be used as a new approach and an alternative to the typical vibration signal based health monitoring and remaining useful life prediction (RUL), when integrated with machine learning techniques that are regarded as a powerful solution. Measurement system integration with the proposed signal processing method based on decision trees with different types and levels of wavelets for the cutter reliability decision-making process was presented together with proving their ability to trace the tool condition accurately. Prediction errors achieved with the use of different signal sources and data processing methods were presented and compared.
Ahamd A, Paul A, Din S, Rathore MM, Choi GS, Jeon G. - Multilevel data processing using parallel algorithms for analyzing Big Data in high-performance computing. International Journal of Parallel Programming 2018; 46: 508-527,
Arrazola P, Özel T, Umbrello D, Davies M, Jawahir I. - Recent advances in modelling of metal machining processes. CIRP Annals 2013; 62: 695-718,
Borucka A, Wiśniowski P, Mazurkiewicz D, Świderski A. - Laboratory measurements of vehicle exhaust emissions in conditions reproducing real traffic. Measurement 2021; 174: 108998,
Bousdekis A, Lepenioti K, Apostolou D, et al. - Decision making in predictive maintenance: Literature review and research agenda for Industry 4.0. IFAC-PapersOnLine 2019; 52: 607-612, 10.1016/j.ifacol.2019.11.226.
Breiman L, Friedman JH, Olshen RA, Stone CJ. - Classification and Regression Trees. Chapman and Hall/CRC: Boca Raton, FL, USA 1984.
Carleo G, Cirac I, Cranmer K, Daudet L, Schuld M, Tishby N, Vogt-Maranto L, Zdeborova L. - Machine learning and the physical sciences. Reviews of Modern Physics 2019; 91: 045002.
Choi S, Battulga L, Nasridinov A, Yoo K-H. - A Decision Tree Approach for Identifying Defective Products in the Manufacturing Process. International Journal of Contents 2017; 13: 57–65,
Costa EP, Lorena AC, Carvalho ACPLF, Freitas AA. - A review of performance evaluation measures for hierarchical classifiers. Evaluation Methods for Machine Learning II: papers from the AAAI-2007 Workshop, AAAI Press 2007: 82–196.
Dargan S, Kumar M, Ayyagari MR, Kumar G. - A survey of deep learning and its applications: a new paradigm to machine learning. Archives of Computational Methods in Engineering 2020; 27: 1071-1092,
Daubechies I. - Orthonormal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics 1998; 41: 909–996.
Daubechies I. - Ten lectures on wavelets. Society for Industrial and Applied Mathematics 1992,
Edwards T. - Discrete Wavelets Transform: Theory and Implementation. Stanford University 1991.
Farid DM, Zhang L, Rahman CM, Hossain MA, Strachan R. - Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Systems with Applications 2014; 41: 1937–1946,
Fawcett T. - An introduction to ROC analysis. Pattern Recognition Letters 2006; 27: 861–874,
Fawcett T. - Using rule sets to maximize ROC performance. Proceedings 2001 IEEE International Conference on Data Mining ICDM 2001: 131–138, 10.1109/ICDM.2001.989510.
Goyal D, Pabla B. - The vibration monitoring methods and signal processing techniques for structural monitoring: a review. Archives of Computational Methods in Engineering 2016; 23: 585-594, 10.1007/s11831-015-9145-0.
Guo Y, Wang N, Xu ZY, Wu K. - The internet of things-based decision support system for information processing in intelligent manufacturing using data mining technology. Mechanical Systems and Signal Processing 2020; 142: 106630,
Hastie T, Tibshirani R, Friedman J. - The elements of statistical learning. Springer-Verlag New York Inc 2009, 10.1007/978-0-387-84858-7.
Hssina B, Merbouha A, Ezzikouri H, Erritali M. - A comparative study of decision tree ID3 and C4.5. International Journal of Advanced Computer Science and Applications, Special Issue on Advances in Vehicular Ad Hoc Networking and Applications 2014; 4: 13–19.
James G, Witten D, Hastie T, Tibshirani R. - An introduction to statistical learning. Springer-Verlag GmbH 2013,
Jasiulewicz‐Kaczmarek M, Antosz K, Wyczółkowski R, Mazurkiewicz D, Sun B, Qian C, Ren Y. - Application of MICMAC, Fuzzy AHP and Fuzzy TOPSIS for Evaluation of the Maintenance Factors Affecting Sustainable Manufacturing. Energies 2021; 14(1436): 1-31,
Jiang S, Sun SY. - Stability analysis for a milling system considering multi-point-contact cross-axis mode coupling and cutter run-out effects. Mechanical Systems and Signal Processing 2020; 141: 106452,
Koch W. - Tracking and sensor data fusion. Methodological framework and selected applications. Springer Verlag, Berlin 2014.
Kozłowski E, Mazurkiewicz D, Żabiński T, Prucnal S, Sęp J. - Assessment model of cutting tool condition for real-time supervision system. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2019; 21: 679-685,
Kozłowski E, Mazurkiewicz D, Żabiński T, Prucnal S, Sęp J. - Machining sensor data management for operation-level predictive model. Expert Systems with Applications 2020; 159: 1-22,
Lepenioti K, Bousdekis A, Apostolou D, Mentzas G. - Prescriptive analytics: literature review and research challenges. International Journal of Information Management 2020; 50: 57-70,
Li H, Wang W, Li Z, Dong L, Li Q. - A novel approach for predicting tool remaining useful life using limited data. Mechanical Systems and Signal Processing 2020; 143: 1086832,
Liu R, Kothuru A, Zhang S. - Calibration-based tool condition monitoring for repetitive machining operations. Journal of Manufacturing Systems 2020; 54: 285-293,
Matthews BW. - Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA) - Protein Structure 1975; 405: 442-451,
Mazurkiewicz D. - Empirical and analytical models of cutting process of rocks. Journal of Mining Science 2000; 36: 481-486,
Nath C. - Integrated tool condition monitoring systems and their applications: a comprehensive review. Procedia Manufacturing 2020; 48:852-863,
Neugebauer R, Denkena B, Wegener K. - Mechatronic systems for machine tools. CIRP Annals 2007; 56: 657–686,
Nouri M, Fussell BK, Ziniti BL, Linder E. - Real-time tool wear monitoring in milling using a cutting condition independent method. International Journal of Machine Tools and Manufacture 2015; 89: 1-13,
Pelayo GU, Trejo DO. - Model-based phase shift optimization of serrated end mills: minimizing forces and surface location error. Mechanical Systems and Signal Processing 2020; 144: 106860,
Percival DB, Walden AT. - Wavelet methods for time series analysis. Cambridge University Press 2000.
Powers DM. - Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technology 2011; 2: 37-63.
Provost F, Fawcett T, Kohavi R. - The case against accuracy estimation for comparing classifiers, Proceedings of the ICML-98, Morgan Kaufmann, San Francisco 1998: 445-453.
Raghavan V. - Application of decision trees for integrated circuit yield improvement. In the 13th Annual IEEE/SEMI Advanced Semiconductor Manufacturing Conference, Advancing the Science and Technology of Semiconductor Manufacturing ASMC 2002; 02CH37259: 262–265, 10.1109/ASMC.2002.1001615.
Ronowicz J, Thommes M, Kleinebudde P, Krysiński J. - A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm. European Journal of Pharmaceutical Sciences 2015; 73: 44–48,
Schuld M, Sinayski I, Petruccione F. - An introduction to quantum machine learning. Contemporary Physics 2015; 56: 172-185, https://doi. org/10.1080/00107514.2014.964942.
Shao Q, Rowe RC, York P. - Comparison of neurofuzzy logic and decision trees in discovering knowledge from experimental data of an immediate release tablet formulation. European Journal of Pharmaceutical Sciences 2017; 31: 129–136,
Sokolova M, Japkowicz N, Szpakowicz S. - Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation. In: Sattar A., Kang B. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006, Lecture Notes in Computer Science 2006; 4304,
Vamsi I, Sabareesh GR, Penumakala PK. - Comparison of condition monitoring techniques in assessing fault severity for a wind turbine gearbox under non-stationary loading. Mechanical Systems and Signal Processing 2019; 124: 1-20,
Walnut DF. - An introduction to wavelet analysis. Springer Nature 2004.
Wang Y, Zheng L, Wang Y. - Event-driven tool condition monitoring methodology considering tool life prediction based on industrial internet. Journal of Manufacturing Systems 2021; 58: 205-222,
Wu X, Kumar V, Ross J, Quinlan J, et al. - Top 10 algorithms in data mining. Knowledge and Information Systems 2008; 14: 1–37,
Xi S, Cao H, Chen X. - Dynamic modelling of spindle bearing system and vibration response investigations. Mechanical Systems and Signal Processing 2018; 114: 486-511,
Zhang C, Qian Y, Dui H, Wang S, Chen R, Tomovic MM. - Opportunistic maintenance strategy of a Heave Compensation System for expected performance degradation. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23(3): 512–521,
Zhang C, Yao X, Zhang J, Jin H. - Tool condition monitoring and remaining useful life prognostic based on wireless sensor in dry milling operations. Sensors 2016: 16: 795,
Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX. - Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing 2019; 115: 213-237,
Żabiński T, Mączka T, Kluska J. - Industrial Platform for Rapid Prototyping of Intelligent Diagnostic Systems. Trends in Advanced Intelligent Control, Optimization and Automation - Polish Control Conference, eds. W. Mitkowski, J. Kacprzyk, K. Oprzędkiewicz, P. Skruch 2017: 712-721,
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