Unlocking the potential of “big data” and advanced analytics in ATE

Abstract

Big Data and advanced analytics capabilities are delivering value in many commercial sectors. The motivation for implementing this new technology is having the ability to conduct analysis of big data to achieve cost reductions, business process improvements, faster and better decisions, and new offerings for customers. These key business objectives also apply to the domain of Automatic Test Equipment (ATE). It is clear that big data and advanced analytics technologies have the potential to bring dramatic improvements to the DoD ATE Community of Interest (COI). However, in order to unlock the potential of Big Data and advanced analytics in ATE, we have to deal with some fundamental issues that impede their implementation. For example, currently there is no connectivity or integration of Unit Under Test (UUT) test results or health monitoring data produced by the system itself to the troubleshooting, test and repair data produced throughout the maintenance process or test data produced by the ATE. Also, there is no standard format or interface employed for capturing, storing, managing and accessing the health state data produced by the ATE. Data collected across operational maintenance activities is in numerous non-standard formats, making it difficult to correlate and aggregate to support advanced analytics. This paper discusses the fundamental shift in business practice required to address these critical issues, the specific benefits that can result from the integration of Big Data and advanced analytics in ATE, including enabling Prognostics and Health Management (PHM). The paper also provides an overview description of a specific case study, the application of ATML standards in the approach, and some critical design and implementation issues based on current (actual) development efforts.