Abstract
Condition-based maintenance (CBM) has emerged as a proactive strategy for determining the best time for maintenance activities. In this paper, a case of a milling process with imperfect maintenance at a German automotive manufacturer is considered. Its major challenge is that only data with missing labels are available, which does not provide a sufficient basis for classical prognostic maintenance models. To overcome this shortcoming, a data science study is carried out that combines several analytical methods, especially from the field of machine learning (ML). These include time-domain and time–frequency domain techniques for feature extraction, agglomerative hierarchical clustering and time series clustering for unsupervised pattern detection, as well as a recurrent neural network for prognostic model training. With the approach developed, it is possible to replace decisions that were made based on subjective criteria with data-driven decisions to increase the tool life of the milling machines. The solution can be employed beyond the presented case to similar maintenance scenarios as the basis for decision support and prognostic model development. Moreover, it helps to further close the gap between ML research and the practical implementation of CBM.
Similar content being viewed by others
References
Aghabozorgi S, Shirkhorshidi AS, Wah TY (2015) Time-series clustering – a decade review. Inform Syst 53:16–38
Ahmad R, Kamaruddin S (2012) An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng 63(1):135–149
Baruah P, Chinnam RB, Filev D (2006) An autonomous diagnostics and prognostics framework for condition-based maintenance. In: International joint conference on neural networks, pp 3428–3435
Bousdekis A, Magoutas B, Apostolou D, Mentzas G (2015) Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance. J Intell Manuf 115:1225–1250
Breiman L (2001) Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat Sci 16(3):199–231
Brock G, Pihur V, Datta S, Datta S (2008) clValid: an R package for cluster validation. J Stat Softw 25(4):1–22
Bumblauskas D, Gemmill D, Igou A, Anzengruber J (2017) Smart maintenance decision support systems (SMDSS) based on corporate big data analytics. Expert Syst Appl 90:303–317
Cartella F, Lemeire J, Dimiccoli L, Sahli H (2015) Hidden semi-markov models for predictive maintenance. Math Probl Eng 2015:278120
Cheng GQ, Zhou BH, Li L (2018) Integrated production, quality control and condition-based maintenance for imperfect production systems. Reliab Eng Syst Safe 175:251–264
Choudhary AK, Harding JA, Tiwari MK (2009) Data mining in manufacturing: a review based on the kind of knowledge. J Intell Manuf 20(5):501–521
Cline B, Niculescu RS, Huffman D, Deckel B (2017) Predictive maintenance applications for machine learning. In: Reliability and maintainability symposium (RAMS), 2017 Annual. IEEE, pp 1–7
Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74(366):427–431
Dragomir OE, Gouriveau R, Dragomir F, Minca E, Zerhouni N (2009) Review of prognostic problem in condition-based maintenance. In: European control conference. IEEE control systems society. Budapest, pp 1587–1592
Elattar HM, Elminir HK, Riad AM (2016) Prognostics: a literature review. Complex Intell Syst 2(2):125–154
Everitt BS, Landau S, Leese M, Stahl D (2011) Cluster analysis. Wiley Series in Probability and Statistics, New York
Galar D, Kumar U, Lee J, Zhao W (2012) Remaining useful life estimation using time trajectory tracking and support vector machines. J Phys: Conf Ser 364:1–10
Gouriveau R, Ramasso E, Zerhouni N (2013) Strategies to face imbalanced and unlabelled data in PHM applications. Chem Eng Trans 33:115–120
Goyal D, Pabla BS (2015) Condition based maintenance of machine tools – a review. CIRP J Manuf Sci Technol 10:24–35
Gregor S, Hevner AR (2013) Positioning and presenting design science research for maximum impact. MIS Q 37(2):337–355
Hansen BE (2000) Sample splitting and threshold estimation. Econometrica 68(3):575–603
Heimes FO (2008) Recurrent neural networks for remaining useful life estimation. In: International conference on prognostics and health management, pp 1–6
Huang R, Xi L, Li X, Liu CR, Qiu H, Lee J (2007) Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mech Syst Signal Process 21(1):193–207
Jämsä-Jounela SL, Vermasvuori M, Endén P, Haavisto S (2003) A process monitoring system based on the Kohonen self-organizing maps. Control Eng Pract 11(1):83–92
Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510
Khawaja T, Vachtsevanos G, Wu B (2005) Reasoning about uncertainty in prognosis: a confidence prediction neural network approach. In: Annual meeting of the north american fuzzy information processing society, pp 7–12
Kurgan LA, Musilek P (2006) A survey of knowledge discovery and data mining process models. Know Eng Rev 21(1):1–24
Leturiondo U, Salgado O, Ciani L, Galar D, Catelani M (2017) Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data. Measurement 108:152–162
Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2011) Big data: the next frontier for innovation, competition, and productivity. Technical Report McKinsey Global Institute
Meeker WQ, Hong Y (2014) Reliability meets big data: opportunities and challenges. Qual Eng 26(1):102–116
Muchiri P, Pintelon L, Gelders L, Martin H (2011) Development of maintenance function performance measurement framework and indicators. Int J Prod Econ 131(1):295–302
Pan Y, Er MJ, Li X, Yu H, Gouriveau R (2014) Machine health condition prediction via online dynamic fuzzy neural networks. Eng Appl Artif Intel 35:105–113
Peng Y, Dong M, Zuo MJ (2010) Current status of machine prognostics in condition-based maintenance: a review. Int J Adv Manuf Tech 50(1):297–313
Ramasso E, Saxena A (2014) Performance benchmarking and analysis of prognostic methods for CMAPSS datasets. Int J Progn Health Manag 5(2):1–15
Rinaldo A, Wasserman L, G’Sell M, Lei J (2016) Bootstrapping and sample splitting for high-dimensional, assumption-free inference. ArXiv Preprint: arxiv:1611.05401
Saxena A, Goebel K (2008) C-MAPSS data set. NASA Ames Prognostics Data Repository
Shmueli G, Koppius OR (2011) Predictive analytics in information systems research. MIS Q35(3):553–572
Si XS, Wang W, Hu CH, Zhou DH (2011) Remaining useful life estimation – a review on the statistical data driven approaches. Eur J Oper Res 213(1):1–14
Sneath PHA, Sokal RR (1973) Numerical taxonomy. The Principles and Practice of Numerical Classification. Freeman, San Francisco
Susto GA, Schirru A, Pampuri S, McLoone S, Beghi A (2015) Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans Ind Inform 11(3):812–820
Tian Z, Wong L, Safaei N (2010) A neural network approach for remaining useful life prediction utilizing both failure and suspension histories. Mech Syst Signal Process 24(5):1542–1555
Ullah I, Yang F, Khan R, Liu L, Yang H, Gao B, Sun K (2017) Predictive maintenance of power substation equipment by infrared thermography using a machine-learning approach. Energy 10(12):1–13
Van der Laan M, Pollard K, Bryan J (2003) A new partitioning around medoids algorithm. J Stat Comput Simul 73(8):575–584
Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999
Veldman J, Wortmann H, Klingenberg W (2011) Typology of condition based maintenance. J Qual Maint Eng 17(2):183–202
Vogl GW, Weiss BA, Helu M (2016) A review of diagnostic and prognostic capabilities and best practices for manufacturing. J Intell Manuf pp 1–17
Wang W, Christer AH (2000) Towards a general condition based maintenance model for a stochastic dynamic system. J Oper Res Soc 51(2):145–155
Williams RJ (1995) Adaptive state representation and estimation using recurrent connectionist networks. In: Miller WT, Sutton RS, Werbos PJ (eds) Neural networks for control. MIT Press, Cambridge, pp 97–114
Yuan J, Liu X (2013) Semi-supervised learning and condition fusion for fault diagnosis. Mech Syst Signal Process 38(2):615–627
Zhao X, Li M, Xu J, Song G (2011) An effective procedure exploiting unlabeled data to build monitoring system. Expert Syst Appl 38(8):10199–10204
Zschech P (2018) A taxonomy of recurring data analysis problems in maintenance analytics. In: Proceedings of the 26th European conference on information systems
Author information
Authors and Affiliations
Corresponding author
Additional information
Accepted after two revisions by the editors of the special issue.
Rights and permissions
About this article
Cite this article
Zschech, P., Heinrich, K., Bink, R. et al. Prognostic Model Development with Missing Labels. Bus Inf Syst Eng 61, 327–343 (2019). https://doi.org/10.1007/s12599-019-00596-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12599-019-00596-1