1. A Holistic Quality Assurance Approach for Machine Learning Applications in Cyber-Physical Production Systems
- Author
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Hajo Wiemer, Alexander Dementyev, Steffen Ihlenfeldt, and Publica
- Subjects
Technology ,QH301-705.5 ,Computer science ,Process (engineering) ,QC1-999 ,Machine learning ,computer.software_genre ,data-driven methods ,manufacturing data management ,process optimization ,General Materials Science ,Biology (General) ,QD1-999 ,Instrumentation ,Protocol (object-oriented programming) ,Fluid Flow and Transfer Processes ,business.industry ,Physics ,Process Chemistry and Technology ,General Engineering ,Cyber-physical system ,data mining ,Engineering (General). Civil engineering (General) ,Automation ,Computer Science Applications ,Chemistry ,machine learning ,Data quality ,Transparency (graphic) ,Applications of artificial intelligence ,Artificial intelligence ,TA1-2040 ,business ,data quality assurance ,Quality assurance ,computer - Abstract
With the trend of increasing sensors implementation in production systems and comprehensive networking, essential preconditions are becoming required to be established for the successful application of data-driven methods of equipment monitoring, process optimization, and other relevant automation tasks. As a protocol, these tasks should be performed by engineers. Engineers usually do not have enough experience with data mining or machine learning techniques and are often skeptical about the world of artificial intelligence (AI). Quality assurance of AI results and transparency throughout the IT chain are essential for the acceptance and low-risk dissemination of AI applications in production and automation technology. This article presents a conceptual method of the stepwise and level-wise control and improvement of data quality as one of the most important sources of AI failures. The appropriate process model (V-model for quality assurance) forms the basis for this.
- Published
- 2021