In semiconductor manufacturing, monitoring equipment status is vital in ensuring process stability and efficient equipment operation. Thus, there is an increasing need for a health index that can efficiently reflect the overall condition of the equipment as a unified metric. Previous studies have derived a health index based on real-time time-series data collected from single wafer processing, overlooking other significant perspectives essential for practical equipment monitoring. Parameters such as temperature and pressure with similar trends in time-series data of multiple consecutive wafers indicate that they are sensitive to the cumulative use of equipment and are similarly affected by equipment aging. Hence, it is necessary to consider the similarity of trends for efficient diagnosis and control. The type of physical components such as chambers, pumps, and pipes observed by each parameter is also an important criterion for accurately understanding the condition of the equipment and taking necessary actions. These can be considered as monitoring views. Therefore, this study introduces a new health index framework for monitoring semiconductor equipment, utilizing multi-view data. We propose three key views for monitoring and define multi-view data by suggesting a quantitative method to represent it in graphical form. Based on it, we propose a method for deriving the health index using an autoencoder-based approach and dynamic weighting techniques. We have validated the effectiveness of our approach with real datasets, demonstrating its potential as a valuable tool for monitoring the condition of semiconductor equipment.