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It is an integrated system that merges computing, sensors, and actuators, controlled by computer-based algorithms that integrate people and cyberspace. However, CPS performance is limited by its computational complexity. Finding a way to implement CPS with reduced complexity while incorporating more efficient diagnostics, forecasting, and equipment health management in a real-time performance remains a challenge. Therefore, the study proposes an integrative machine-learning method to reduce the computational complexity and to improve the applicability as a virtual subsystem in the CPS environment. This study utilizes random forest (RF) and a time-series deep-learning model based on the long short-term memory (LSTM) networking to achieve real-time monitoring and to enable the faster corrective adjustment of machines. We propose a method in which a fault detection alarm is triggered well before a machine fails, enabling shop-floor engineers to adjust its parameters or perform maintenance to mitigate the impact of its shutdown. As demonstrated in two empirical studies, the proposed method outperforms other times-series techniques. Accuracy reaches 80% or higher 3 h prior to real-time shutdown in the first case, and a significant improvement in the life of the product (281%) during a particular process appears in the second case. The proposed method can be applied to other complex systems to boost the efficiency of machine utilization and productivity.<\/jats:p>","DOI":"10.1115\/1.4045663","type":"journal-article","created":{"date-parts":[[2019,12,11]],"date-time":"2019-12-11T13:32:21Z","timestamp":1576071141000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":35,"title":["An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System"],"prefix":"10.1115","volume":"20","author":[{"given":"Ming-Chuan","family":"Chiu","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering and Engineering Management, National Tsing-Hua University, Kuang-Fu Road, Hsinchu, Hsinchu County 30013, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chien-De","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Engineering Management, National Tsing-Hua University, Kuang-Fu Road, Hsinchu, Hsinchu County 30013, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tung-Lung","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Engineering Management, National Tsing-Hua University, Kuang-Fu Road, Hsinchu, Hsinchu County 30013, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2020,1,3]]},"reference":[{"key":"2021022706141600000_CIT0001","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.procir.2017.03.311","article-title":"Towards Industry 4.0 Utilizing Data-Mining Techniques: A Case Study on Quality Improvement","volume":"63","author":"Oliff","year":"2017","journal-title":"Procedia CIRP"},{"issue":"4","key":"2021022706141600000_CIT0002","doi-asserted-by":"crossref","first-page":"16","DOI":"10.24840\/2183-0606_003.004_0003","article-title":"The Industry 4.0 Revolution and the Future of Manufacturing Execution Systems (MES)","volume":"3","author":"Almada-Lobo","year":"2016","journal-title":"J. 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