Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing



Published Nov 13, 2020
Xiaoning Jin Brian A. Weiss David Siegel Jay Lee


The goals of this paper are to 1) examine the current practices of diagnostics, prognostics, and maintenance employed by United States (U.S.) manufacturers to achieve productivity and quality targets and 2) to understand the present level of maintenance technologies and strategies that are being incorporated into these practices. A study is performed to contrast the impact of various industry-specific factors on the effectiveness and profitability of the implementation of prognostics and health management technologies, and maintenance strategies using both surveys and case studies on a sample of U.S. manufacturing firms ranging from small to mid-sized enterprises (SMEs) to large-sized manufacturing enterprises in various industries. The results obtained provide important insights on the different impacts of specific factors on the successful adoption of these technologies between SMEs and large manufacturing enterprises. The varying degrees of success with respect to current maintenance programs highlight the opportunity for larger manufacturers to improve maintenance practices and consider the use of advanced prognostics and health management (PHM) technology. This paper also provides the existing gaps, barriers, future trends, and roadmaps for manufacturing PHM technology and maintenance strategy.

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prognostics and health management, manufacturing system, Maintenance Strategy

Aliustaoglu, C., Ertunc, H. M., & Ocak, H. (2009). Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system, Mechanical Systems and Signal Processing, 23:2, 539-546.
Amer, W., Grosvenor, R., & Prickett, P. (2007). Machine tool condition monitoring using sweeping filter techniques, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 221:1, 103-117.
Barajas, L. G., & Srinivasa, N. (2008). Real-time diagnostics, prognostics health management for largescale manufacturing maintenance systems, Proc. ASME International Manufacturing Science and Engineering Conference, ASME Foundation, 85-94.
Barbera, F., Schneider, H., & Kelle, P. (1996). A condition based maintenance model with exponential failures and fixed inspection intervals, Journal of the Operational Research Society, 1037-1045.
Brambilla, D. , Capisani, L. M., Ferrara, A., & Pisu, P. (2008). Fault detection for robot manipulators via second-order sliding modes, Industrial Electronics, IEEE Transactions on, 55:11, 3954-3963.
Cao, H., Niu, L., & He, Z. (2012). Method for vibration response simulation and sensor placement optimization of a machine tool spindle system with a bearing defect, Sensors, 12:7, 8732-8754.
Chen, D., & Trivedi, K. S. (2002). Closed-form analytical results for condition-based maintenance, Reliability Engineering & System Safety, 76:1, 43-51.
Coble, J., Ramuhalli, P., Bond, L., & Hines, J.W., Upadhyaya, B. (2015). A Review of Prognostics and Health Management Applications in Nuclear Power Plants, International Journal of Prognostics and Health Management, 6 (Special Issue Nuclear Energy PHM) 016, 22.
Daniel, Wayne W. (1990). Spearman rank correlation coefficient. Applied Nonparametric Statistics (2nd ed.). Boston: PWS-Kent. pp. 358–365.
Demetgul, M. (2013). Fault diagnosis on production systems with support vector machine and decision trees algorithms, The International Journal of Advanced Manufacturing Technology, 67:9-12, 2183-2194.
Edwards, A. L. (1976). The Correlation Coefficient. Ch. 4 in An Introduction to Linear Regression and Correlation. San Francisco, CA: W. H. Freeman, pp. 33-46,
Helu, M., T. Hedberg, (2015). Enabling Smart Manufacturing Research and Development using a Product Lifecycle Test Bed. Procedia Manufacturing, 1, 86-97.
Giriraj, M., S. Muthu, (2010). Layerless manufacturing & SAP creating responsive shop floor in the supply chain, Internatinoal Journal of Engineering and Technology, 2(2): 59-64
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, 20:7, 1483-1510.
Jin, X., Siegel, D., Weiss, B., Gamel, E., Wang, W., Lee, J., & Ni, J. (2016). Manufacturing Review (forthcoming)
Jonsson P. & M. Lesshammar, (1999). Evaluation and improvement of manufacturing performance measurement systems – the role of OEE, International Journal of Operations & Production Management, 19(1), 55-78.
Karnouskos, S., D. Savio, P. Spiess, D. Guinard, V. Trifa, & O. Baecker, Real-world service interaction with enterprise systems in dynamic manufacturing environments, Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management, Part of the series Springer Series in Advanced Manufacturing, pp 423-457.
Lee, J., Atat, H., Siegel, D. (2011). A Systematic Methodology for Gearbox Health Assessment and Fault Classification, International Journal of Prognostics and Health Management, 2 (1) 002,16.
Lee, J., Siegel, D., & Lapira, E. R. (2013). Development of a Predictive and Preventive Maintenance Demonstration System for a Semiconductor Etching Tool, ECS Transactions, 52:1, 913-927.
Liao, L., & Pavel, R. (2012). Machine tool feed axis health monitoring using plug-and-prognose technology, Proc. Proceedings of the 2012 Conference of the Society for Machinery Failure Prevention Technology.
Liker, J. K. Is OEE a Useful Key Performance Indicator? Industry Week. (2014, March 7).
Liu, H., & Coghill, G. M. (2005). A model-based approach to robot fault diagnosis, Knowledge-Based Systems, 18:4, 225-233.
Malekian, M., Park, S., & Jun, M. B. (2009). Tool wear monitoring of micro-milling operations, Journal of Materials Processing Technology, 209:10, 4903-4914.
Mann, L., Saxena, A., & Knapp, G. M. (1995). Statisticalbased or condition-based preventive maintenance, Journal of Quality in Maintenance Engineering, 1:1,46-59.
Marseguerra, M., Zio, E., & Podofillini, L. (2002). Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation, Reliability Engineering & System Safety, 77:2, 151-165.
Measurement Science Roadmap for Prognostics and Health Management for Smart Manufacturing Systems, National Institute of Standards and Technology (NIST), August 2015.
Muchiri, P. N., Pintelon, L., Martin, H., & De Meyer, A. M. (2010). Empirical analysis of maintenance performance measurement in Belgian industries. International Journal of Production Research, 48(20), 5905-5924.
Muthiah, K. M., Huang, S. H., & Mahadevan, S. (2008). Automating factory performance diagnostics using overall throughput effectiveness (OTE) metric, The International Journal of Advanced Manufacturing Technology, 36:7-8, 811-824.
Nakajima, S. (1988). Introduction to TPM: Total productive maintenance, Productivity Press, Inc , P.O. Box 3007, Cambridge, Massachusetts 02140, USA, 129.
Samah, A., Shahzad, M. K., Zamai, E., Hubac, S. (2015). Effective Maintenance by Reducing Failure-Cause Misdiagnosis in Semiconductor Industry (SI), International Journal of Prognostics and Health Management, 6 (1), 18.
Sjöstrand, N., Blanc, D., & Tavallaey, S. S. (2010). Method and a control system for monitoring the condition of an industrial robot, Google Patents.
Skirtich, T. (2012). A comparative study of prognostic and health assessment methods in sensor rich and sensorless environments, University of Cincinnati. ProQuest Dissertations Publishing, 1515047.
Sohal, A., Olhager, J., O’Neill, P., & Prajogo, D. (2010). Implementation of OEE–issues and challenges, Competitive and Sustainable Manufacturing Products and Services, 1-8.
Sztendel, S., Pislaru, C., & Longstaff, A. P., Fletcher, S., & Myers, A. (2012). Five-Axis Machine Tool Condition Monitoring Using dSPACE Real-Time System, Proc. Journal of Physics: Conference Series, IOP Publishing, 012091.
Vogl, G. W., & Donmez, M. A. (2015-1). A defect-driven diagnostic method for machine tool spindles. CIRP Annals-Manufacturing Technology, 64(1), 377-380.
Vogl, G. W., Weiss, B. A., & Donmez, M. A. A. (2015-2). A Sensor-Based Method for Diagnostics of Machine Tool Linear Axes. Annual Conference of the Prognostics and Health Management Society.
Weiss, B.A., Vogl, G. W., Helu, M. H., Qiao, G., Pellegrino, J., Justiniano, M., & Raghunathan, A. (2015). Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems: Key Findings from a Roadmapping Workshop. Annual Conference of the Prognostics and Health Management Society.
Zhou, Y., Tao, T., Mei, X., Jiang, G., & Sun, N. (2011). Feed-axis gearbox condition monitoring using built-in position sensors and EEMD method, Robotics and Computer-Integrated Manufacturing, 27:4, 785-793.
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