A Predictive Model Based on Transformer with Statistical Feature Embedding in Manufacturing Sensor Dataset
This work addresses the problem of insufficient data for predictive modeling in manufacturing industries, offering an incremental improvement for process management and yield enhancement.
The study tackled the challenge of building robust predictive models from limited manufacturing sensor data by proposing a Transformer-based model with statistical feature embedding and window positional encoding, achieving superior results in fault detection and virtual metrology compared to baseline models.
In the manufacturing process, sensor data collected from equipment is crucial for building predictive models to manage processes and improve productivity. However, in the field, it is challenging to gather sufficient data to build robust models. This study proposes a novel predictive model based on the Transformer, utilizing statistical feature embedding and window positional encoding. Statistical features provide an effective representation of sensor data, and the embedding enables the Transformer to learn both time- and sensor-related information. Window positional encoding captures precise time details from the feature embedding. The model's performance is evaluated in two problems: fault detection and virtual metrology, showing superior results compared to baseline models. This improvement is attributed to the efficient use of parameters, which is particularly beneficial for sensor data that often has limited sample sizes. The results support the model's applicability across various manufacturing industries, demonstrating its potential for enhancing process management and yield.