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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2407.21503 |
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| _version_ | 1866913454038712320 |
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| author | Gram, Jonas Sai, Brandon K. Bauernhansl, Thomas |
| author_facet | Gram, Jonas Sai, Brandon K. Bauernhansl, Thomas |
| contents | In today's rapidly evolving landscape of automation and manufacturing systems, the efficient resolution of productivity losses is paramount. This study introduces a data-driven ensemble approach, utilizing the cyclic multivariate time series data from binary sensors and signals from Programmable Logic Controllers (PLCs) within these systems. The objective is to automatically analyze productivity losses per cycle and pinpoint their root causes by assigning the loss to a system element. The ensemble approach introduced in this publication integrates various methods, including information theory and machine learning behavior models, to provide a robust analysis for each production cycle. To expedite the resolution of productivity losses and ensure short response times, stream processing becomes a necessity. Addressing this, the approach is implemented as data-stream analysis and can be transferred to batch processing, seamlessly integrating into existing systems without the need for extensive historical data analysis. This method has two positive effects. Firstly, the result of the analysis ensures that the period of lower productivity is reduced by identifying the likely root cause of the productivity loss. Secondly, these results are more reliable due to the ensemble approach and therefore avoid dependency on technical experts. The approach is validated using a semi-automated welding manufacturing system, an injection molding automation system, and a synthetically generated test PLC dataset. The results demonstrate the method's efficacy in offering a data-driven understanding of process behavior and mark an advancement in autonomous manufacturing system analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21503 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Root Cause Analysis Of Productivity Losses In Manufacturing Systems Utilizing Ensemble Machine Learning Gram, Jonas Sai, Brandon K. Bauernhansl, Thomas Machine Learning In today's rapidly evolving landscape of automation and manufacturing systems, the efficient resolution of productivity losses is paramount. This study introduces a data-driven ensemble approach, utilizing the cyclic multivariate time series data from binary sensors and signals from Programmable Logic Controllers (PLCs) within these systems. The objective is to automatically analyze productivity losses per cycle and pinpoint their root causes by assigning the loss to a system element. The ensemble approach introduced in this publication integrates various methods, including information theory and machine learning behavior models, to provide a robust analysis for each production cycle. To expedite the resolution of productivity losses and ensure short response times, stream processing becomes a necessity. Addressing this, the approach is implemented as data-stream analysis and can be transferred to batch processing, seamlessly integrating into existing systems without the need for extensive historical data analysis. This method has two positive effects. Firstly, the result of the analysis ensures that the period of lower productivity is reduced by identifying the likely root cause of the productivity loss. Secondly, these results are more reliable due to the ensemble approach and therefore avoid dependency on technical experts. The approach is validated using a semi-automated welding manufacturing system, an injection molding automation system, and a synthetically generated test PLC dataset. The results demonstrate the method's efficacy in offering a data-driven understanding of process behavior and mark an advancement in autonomous manufacturing system analysis. |
| title | Root Cause Analysis Of Productivity Losses In Manufacturing Systems Utilizing Ensemble Machine Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.21503 |