Detecting gradual trends: Integrating EWMA control charts with artificial intelligence algorithms (LSTM)

Control charts are widely used in statistical process control (SPC) to detect small, gradual shifts in process behavior, although effective at mitigating noise, such as the exponentially weighted moving average (EWMA). Traditional EWMAs, however, face significant challenges and limited adaptability in complex and dynamic environments. In this paper, we propose an improved hybrid approach that integrates EWMAs with artificial intelligence algorithms, such as anomaly detection models, deep learning networks, and unsupervised learning, to enhance the early detection of non-random variations and subtle process trends. Simulations and real-world datasets were used to validate the effectiveness of the integrated model in identifying slow-developing faults.