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Maintenance has largely remained a human-knowledge centered activity, with the primary records of activity being textbased maintenance work orders (MWOs). However, the bulk of maintenance research does not currently attempt to quantify human knowledge, though this knowledge can be rich with useful contextual and system-level information. The underlying quality of data in MWOs often suffers from misspellings, domain-specific (or even workforce specific) jargon, and abbreviations, that prevent its immediate use in computer analyses. Therefore, approaches to making this data computable must translate unstructured text into a formal schema or system; i.e., perform a mapping from informal technical language to some computable format. Keyword spotting (or, extraction) has proven a valuable tool in reducing manual efforts while structuring data, by providing a systematic methodology to create computable knowledge. This technique searches for known vocabulary in a corpus and maps them to designed higher level concepts, shifting the primary effort away from structuring the MWOs themselves, toward creating a dictionary of domain specific terms and the knowledge that they represent. The presented work compares rules-based keyword extraction to data-driven tagging assistance, through quantitative and qualitative discussion of the key advantages and disadvantages. This will enable maintenance practitioners to select an appropriate approach to information encoding that provides needed functionality at minimal cost and effort.

Recent efforts in smart manufacturing (SM) have proven quite effective at elucidating system behavior using sensing systems, communications, and computational platforms, along with statistical methods to collect and analyze the real-time performance data. However, how do you effectively select where and when to implement these technology solutions within manufacturing operations? Furthermore, how do you account for the human-driven activities in manufacturing when inserting new technologies? Due to a reliance on human problem-solving skills, today’s maintenance operations are largely manual processes without wide-spread automation. The current state-of-the-art maintenance management systems and out-of-the-box solutions do not directly provide necessary synergy between human and technology, and many paradigms ultimately keep the human and digital knowledge systems separate. Decision makers are using one or the other on a case-by-case basis, causing both human and machine to cannibalize each other’s function, leaving both disadvantaged despite ultimately having common goals. A new paradigm can be achieved through a hybridized system approach—where human intelligence is effectively augmented with sensing technology and decision support tools, including analytics, diagnostics, or prognostic tools. While these tools promise more efficient, cost-effective maintenance decisions and improved system productivity, their use is hindered when it is unclear what core organizational or cultural problems they are being implemented to solve. To explicitly frame our discussion about implementation of new technologies in maintenance management around these problems, we adopt well-established error mitigation frameworks from human factors experts—who have promoted human–system integration for decades—to maintenance in manufacturing. Our resulting tiered mitigation strategy guides where and how to insert SM technologies into a human-dominated maintenance management process.