The present status and future growth of maintenance in US manufacturing: results from a pilot survey

A research study was conducted (1) to examine the practices employed by US manufacturers to achieve productivity goals and (2) to understand what level of intelligent maintenance technologies and strategies are being incorporated into these practices. This study found that the effectiveness and choice of maintenance strategy were strongly correlated to the size of the manufacturing enterprise; there were large differences in adoption of advanced maintenance practices and diagnostics and prognostics technologies between small and medium-sized enterprises (SMEs). Despite their greater adoption of maintenance practices and technologies, large manufacturing organizations have had only modest success with respect to diagnostics and prognostics and preventive maintenance projects. The varying degrees of success with respect to preventative maintenance programs highlight the opportunity for larger manufacturers to improve their maintenance practices and use of advanced prognostics and health management (PHM) technology. The future outlook for manufacturing PHM technology among the manufacturing organizations considered in this study was overwhelmingly positive; many manufacturing organizations have current and planned projects in this area. Given the current modest state of implementation and positive outlook for this technology, gaps, future trends, and roadmaps for manufacturing PHM and maintenance strategy are presented.

[1]  Zhengjia He,et al.  Method for Vibration Response Simulation and Sensor Placement Optimization of a Machine Tool Spindle System with a Bearing Defect , 2012, Sensors.

[2]  Martin B.G. Jun,et al.  Tool wear monitoring of micro-milling operations , 2009 .

[3]  Jan Olhager,et al.  Implementation of OEE - Issues and challenges , 2010 .

[4]  David C. Lane,et al.  Invited Review and Reappraisal Industrial Dynamics. , 1997 .

[5]  Linxia Liao,et al.  MACHINE TOOL FEED AXIS HEALTH MONITORING USING PLUG-AND- PROGNOSE TECHNOLOGY , 2012 .

[6]  中嶋 清一,et al.  Introduction to TPM : total productive maintenance , 1988 .

[7]  Luca Podofillini,et al.  Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation , 2002, Reliab. Eng. Syst. Saf..

[8]  Helmut Schneider,et al.  A Condition Based Maintenance Model with Exponential Failures and Fixed Inspection Intervals , 1996 .

[9]  Gedong Jiang,et al.  Feed-axis gearbox condition monitoring using built-in position sensors and EEMD method , 2011 .

[10]  Gerald M. Knapp,et al.  Statistical‐based or condition‐based preventive maintenance? , 1995 .

[11]  Mustafa Demetgul,et al.  Fault diagnosis on production systems with support vector machine and decision trees algorithms , 2013 .

[12]  Narayan Srinivasa,et al.  Real-Time Diagnostics, Prognostics and Health Management for Large-Scale Manufacturing Maintenance Systems , 2008 .

[13]  Kishor S. Trivedi,et al.  Closed-form analytical results for condition-based maintenance , 2002, Reliab. Eng. Syst. Saf..

[14]  H. Metin Ertunc,et al.  Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system , 2009 .

[15]  Jay Lee,et al.  Development of a Predictive and Preventive Maintenance Demonstration System for a Semiconductor Etching Tool , 2013 .

[16]  Christopher C. White,et al.  Focus on Durability, PATH Research at the National Institute of Standards and Technology | NIST , 2001 .

[17]  Seiichi Nakajima,et al.  Introduction to TPM: total productive maintenance , 1988 .

[18]  Crinela Pislaru,et al.  Five-Axis Machine Tool Condition Monitoring Using dSPACE Real-Time System , 2012 .

[19]  Kanthi M.N. Muthiah,et al.  Automating factory performance diagnostics using overall throughput effectiveness (OTE) metric , 2008 .

[20]  Roger Ivor Grosvenor,et al.  Machine tool condition monitoring using sweeping filter techniques , 2007 .

[21]  Joan Pellegrino,et al.  Measurement Science Roadmap for Prognostics and Health Management for Smart Manufacturing Systems , 2016 .

[22]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .