In autonomous manufacturing systems, the performance of time-series-based anomaly detection and fault diagnosis is highly sensitive to window size selection. Conventional approaches rely on empirical rules or fixed window settings, which often fail to capture the diverse temporal characteristics of anomalies and lead to performance degradation. This study systematically addresses the window size selection problem by categorizing anomaly patterns into three representative types: variability, cycle, and local spike. Each pattern is associated with a distinct temporal scale and underlying physical mechanism. Based on this insight, an Anomaly Deviation-Based Window Size Selection (ADW) method is proposed, which quantitatively evaluates anomaly deviation as a function of window size. Unlike traditional preprocessing-oriented approaches, the proposed method redefines window size as a core design variable that directly governs anomaly representation and diagnostic sensitivity. The effectiveness of the ADW method is validated using tension data from a roll-to-roll continuous manufacturing process and vibration data from a rotating bearing fault dataset. Experimental results demonstrate that the proposed approach consistently identifies optimized window sizes tailored to different anomaly types, leading to improved fault classification accuracy and diagnostic robustness. The proposed framework provides a physically interpretable and data-driven guideline for adaptive window size selection in long-term autonomous manufacturing systems.