# Towards transparent and data-driven fault detection in manufacturing: A case study on univariate, discrete time series

arXiv: https://arxiv.org/abs/2507.00102 · HTML: https://mlsanity.com/paper/2507.00102/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [A Benchmark for Interpretability Methods in Deep Neural Networks](https://mlsanity.com/paper/1806.10758/) (2018)
- [Towards a Rigorous Evaluation of XAI Methods on Time Series](https://mlsanity.com/paper/1909.07082/) (2019)
- [timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers](https://mlsanity.com/paper/2007.07606/) (2020)
- [On the Soundness of XAI in Prognostics and Health Management (PHM)](https://mlsanity.com/paper/2303.05517/) (2023)

## Built on by (most cited first, 1 of 1)

- [PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments](https://mlsanity.com/paper/2508.14504/) (2025)

## Abstract (arXiv)

Ensuring consistent product quality in modern manufacturing is crucial, particularly in safety-critical applications. Conventional quality control approaches, reliant on manually defined thresholds and features, lack adaptability to the complexity and variability inherent in production data and necessitate extensive domain expertise. Conversely, data-driven methods, such as machine learning, demonstrate high detection performance but typically function as black-box models, thereby limiting their acceptance in industrial environments where interpretability is paramount. This paper introduces a methodology for industrial fault detection, which is both data-driven and transparent. The approach integrates a supervised machine learning model for multi-class fault classification, Shapley Additive Explanations for post-hoc interpretability, and a do-main-specific visualisation technique that maps model explanations to operator-interpretable features. Furthermore, the study proposes an evaluation methodology that assesses model explanations through quantitative perturbation analysis and evaluates visualisations by qualitative expert assessment. The approach was applied to the crimping process, a safety-critical joining technique, using a dataset of univariate, discrete time series. The system achieves a fault detection accuracy of 95.9 %, and both quantitative selectivity analysis and qualitative expert evaluations confirmed the relevance and inter-pretability of the generated explanations. This human-centric approach is designed to enhance trust and interpretability in data-driven fault detection, thereby contributing to applied system design in industrial quality control.