<p>Anomaly detection is critical in industrial systems for ensuring equipment reliability and improving product quality, especially with the increasing complexity of electronic board production. However, traditional anomaly detection approaches often fail when dealing with high-dimensional data and limited system knowledge. To address this gap, this article aims to develop an effective unsupervised method for anomaly detection suitable for large-scale industrial contexts with minimal prior knowledge. The proposed Multi-block Local Outlier Factor (MLOF) method combines a variable decomposition technique based on Mutual Information and spectral clustering with a local anomaly detection algorithm using the Local Outlier Factor. The method was validated on the Tennessee Eastman Process and real-world industrial cases from Surface Mount Technology production lines, notably by comparing its results with 5 other methods in the literature. Results demonstrate a 15% improvement in anomaly detection performance compared to classical LOF on benchmark data and effective application in detecting anomalies in real production scenarios. The MLOF method represents a significant step forward in anomaly detection for complex systems, offering robust, scalable, and accurate solutions even in data-intensive and knowledge-scarce environments.</p>