A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)

A Survey of Feature Set Reduction Approaches for Predictive Analytics Models in the Connected Manufacturing Enterprise

The broad context of this literature review is the connected manufacturing enterprise, characterized by a data environment such that the size, structure and variety of information strain the capability of traditional software and database tools to effectively capture, store, manage and analyze it. This paper surveys and discusses representative examples of existing research into approaches for feature set reduction in the big data environment, focusing on three contexts: general industrial applications; specific industrial applications such as fault detection or fault prediction; and data reduction. The conclusion from this review is that there is room for research into frameworks or approaches to feature filtration and prioritization, specifically with respect to providing quantitative or qualitative information about the individual features in the dataset that can be used to rank features against each other. A byproduct of this gap is a tendency for analysts not to holistically generalize results beyond the specific problem of interest, and, related, for manufacturers to possess only limited knowledge of the relative value of smart manufacturing data collected

Simulation Analysis of Applicant Scheduling and Processing Alternatives at a Military Entrance Processing Station

Eligibility for enlistment into the US military is assessed by the United States Military Entrance Processing Command (USMEPCOM), an independent agency that reports to the Office of the Secretary of Defense (OSD) and not to any specific branch of military service. This research develops a discrete-event simulation for applicant processing operations at a Military Entrance Processing Station (MEPS) to investigate the viability of potential alternatives to the current applicant arrival and processing operation. Currently, all applicants arrive to the MEPS at the beginning of the processing day in a single batch. This research models and compares two alternatives with the status quo: split-shift processing, by which applicant arrivals occur in two batches: one at 06:00 and one at 11:00 and appointment-based processing, by which applicants may arrive during one of three, four, six, or eight appointment windows. Express-lane processing is also explored, in which applicants are allowed to bypass select processing stations. Experimental results indicate that split-shift processing is not viable under the current processing model due to an unacceptable decrease in applicant throughput. Results from appointment-based scenarios are mixed, with the critical factors being the time between appointment batches and their associated arrival times