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.