|Publication type:||Conference other|
|Type of review:||Not specified|
|Title:||Implementing AI-based innovation in industry|
|Authors:||Goren Huber, Lilach|
|Conference details:||Live-Case-Workshop for EMBA Digital Transformation, University Zurich, 6 July 2021|
|Subjects:||Artificial intelligence; Industry; Innovation; Data science; Deep learning; Machine learning; Predictive maintenance|
|Subject (DDC):||006: Special computer methods |
|Abstract:||The Supervisory Control and Data Acquisition (SCADA) system installed on every wind turbine collects performance and condition data from various components of the turbine in time intervals of 10 minutes. The data is stored and has been used primarily for performance monitoring (identifying losses in the power production) until now. Realizing that this vast amount of historical data from all turbines has a much bigger potential, Nispera decided to launch an innovation project to harvest this potential and offer its clients a new platform for automated detection and localization of technical anomalies and faults in various turbine components. Early detection of faults allows for an intelligent planning of maintenance activities, leading to considerable reduction in the Operation and Maintenance (O&M) expenses of the wind farm operator. “Predictive maintenance” approaches start to replace reactive and preventive approaches to maintenance in a large variety of application fields, ranging from the aircraft industry, through trains, large production machines and public infrastructures. Deploying predictive maintenance algorithms is becoming increasingly attractive owing to the huge progress of the last years regarding machine data availability, cost-effective storage solutions and efficient intelligent algorithms for data analytics, including machine learning and deep learning methods. For Nispera’s clients, predictive maintenance is even more attractive because this service is offered within a more generic platform, which has access to the SCADA data without the need for any new hardware installation. This makes Nispera’s solution cost-effective com-pared to other condition monitoring solutions available on the market. In this way, Nispera directly addresses the needs of wind park owners and operators for continuous monitoring of their turbines, independent of the OEMs.|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Data Analysis and Process Design (IDP)|
|Published as part of the ZHAW project:||Machine Learning Based Fault Detection for Wind Turbines|
|Appears in collections:||Publikationen School of Engineering|
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Goren Huber, L., Acquaviva, M., & Pizza, G. (2021, July 6). Implementing AI-based innovation in industry. Live-Case-Workshop for EMBA Digital Transformation, University Zurich, 6 July 2021.
Goren Huber, L., Acquaviva, M. and Pizza, G. (2021) ‘Implementing AI-based innovation in industry’, in Live-Case-Workshop for EMBA Digital Transformation, University Zurich, 6 July 2021.
L. Goren Huber, M. Acquaviva, and G. Pizza, “Implementing AI-based innovation in industry,” in Live-Case-Workshop for EMBA Digital Transformation, University Zurich, 6 July 2021, Jul. 2021.
Goren Huber, Lilach, et al. “Implementing AI-Based Innovation in Industry.” Live-Case-Workshop for EMBA Digital Transformation, University Zurich, 6 July 2021, 2021.
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