{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T09:46:07Z","timestamp":1744969567539},"reference-count":31,"publisher":"ASME International","issue":"3","license":[{"start":{"date-parts":[[2020,2,19]],"date-time":"2020-02-19T00:00:00Z","timestamp":1582070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,6,1]]},"abstract":"Abstract<\/jats:title>\n Manufacturing industry companies are increasingly interested in using less energy in order to enhance competitiveness and reduce environmental impact. To implement technologies and make decisions that lead to less energy demand, energy\/power data are required. All too often, however, energy data are either not available, or available but too aggregated to be useful, or in a form that makes information difficult to access. Attention herein is focused on this last point. As a step toward greater energy information transparency and smart energy-monitoring systems, this paper introduces a novel, robust time series-based approach to automatically detect and analyze the electrical power cycles of manufacturing equipment. A new pattern recognition algorithm including a power peak clustering method is applied to a large real-life sensor data set of various machine tools. With the help of synthetic time series, it is shown that the accuracy of the cycle detection of nearly 100% is realistic, depending on the degree of measurement noise and the measurement sampling rate. Moreover, this paper elucidates how statistical load profiling of manufacturing equipment cycles as well as statistical deviation analyses can be of value for automatic sensor and process fault detection.<\/jats:p>","DOI":"10.1115\/1.4046208","type":"journal-article","created":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T11:29:51Z","timestamp":1580470191000},"update-policy":"http:\/\/dx.doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":9,"title":["Automatic Detection of Manufacturing Equipment Cycles Using Time Series"],"prefix":"10.1115","volume":"20","author":[{"given":"Jan-Peter","family":"Seevers","sequence":"first","affiliation":[{"name":"Department of Sustainable Products and Processes (UPP), University of Kassel, Kurt-Wolters-Stra\u00dfe 3, Kassel 34125, Germany"}]},{"given":"Kristina","family":"Jurczyk","sequence":"additional","affiliation":[{"name":"Kainos Group plc, Klopstock Street 5, Hamburg 22765, Germany"}]},{"given":"Henning","family":"Meschede","sequence":"additional","affiliation":[{"name":"Department of Sustainable Products and Processes (UPP), University of Kassel, Kurt-Wolters-Stra\u00dfe 3, Kassel 34125, Germany"}]},{"given":"Jens","family":"Hesselbach","sequence":"additional","affiliation":[{"name":"Department of Sustainable Products and Processes (UPP), University of Kassel, Kurt-Wolters-Stra\u00dfe 3, Kassel 34125, Germany"}]},{"given":"John W.","family":"Sutherland","sequence":"additional","affiliation":[{"name":"Department of Environmental and Ecological Engineering, Purdue University, Potter Engineering Center, 500 Central Drive, West Lafayette, IN 47907-2022"}]}],"member":"33","published-online":{"date-parts":[[2020,2,19]]},"reference":[{"key":"2021022709544612500_CIT0001","volume-title":"The Internet of Things: Mapping the Value Beyond the Hype","author":"Manyika","year":"2015"},{"issue":"3\u20134","key":"2021022709544612500_CIT0002","first-page":"803","article-title":"Learning via Acceleration Spectrograms of a DC Motor System with Application to Condition Monitoring.","volume":"106","author":"Lee","year":"2019","journal-title":"Int. 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