Abstract
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.
Highlights
Manufacturers use a combination of reactive maintenance (RM), preventive maintenance (PM), predictive maintenance (PdM), and proactive maintenance (PaM) to maintain their fleet of assets, in which the maintenance strategy for a given asset depends on the complexity of the machine and the impact an unexpected failure has on that machine
It was important to have a grasp on the manufacturers’ current maintenance practices and whether these practices are effective, or if they have room for improvement. This provides some measure of where manufacturing organizations are in terms of maintenance strategy and this could influence their future adoption of more advanced maintenance technologies
While the University of Cincinnati (UC)/University of Michigan (UM) team analyzed the detailed survey data, both teams (UC/UM and National Institute of Standards and Technology (NIST)) assessed the information from the conversational case studies. This team-based approach allowed UC/UM and NIST to jointly formulate what they see as the future directions in prognostic and health management (PHM) given the identified gaps and issues
Summary
Reducing waste, improving equipment up-time, and optimizing product quality are three metrics important to. Equipment and process health states are highly correlated to OEE, there is growing interest in developing intelligent maintenance systems to improve OEE, and predict and prevent unexpected equipment and process downtime. The various maintenance strategies that manufacturers have deployed are in a constant state of evolution given the increasing complexity of manufacturing equipment and processes. Despite the increased interest in prognostics and more advanced maintenance strategies, manufacturers lack a standard process and methodology for using prognostic and health management (PHM) technologies on the shop floor
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