# Root Cause Analysis Of Productivity Losses In Manufacturing Systems Utilizing Ensemble Machine Learning

arXiv: https://arxiv.org/abs/2407.21503 · HTML: https://mlsanity.com/paper/2407.21503/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Revisiting Natural Gradient for Deep Networks](https://mlsanity.com/paper/1301.3584/) (2013)
- [Causal structure based root cause analysis of outliers](https://mlsanity.com/paper/1912.02724/) (2019)
- [Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops](https://mlsanity.com/paper/2303.04038/) (2023)
- [Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontology](https://mlsanity.com/paper/2301.12495/) (2023)
- [Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing](https://mlsanity.com/paper/2310.06306/) (2023)
- [Interactive and Intelligent Root Cause Analysis in Manufacturing with Causal Bayesian Networks and Knowledge Graphs](https://mlsanity.com/paper/2402.00043/) (2024)

## Abstract (arXiv)

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.