Negative-Bias Temperature Instability (NBTI) poses serious threats to modern ICs and may lead to timing and functional failure. If these failures happen at industrial automated production systems, the malfunctioning system can cause significant economic losses due to unacceptable fabrication quality and yield. Although preventive maintenance is a useful way to avoid such a situation, frequently executing preventive maintenance will also introduce significant downtime to the production line. To accurately execute the preventive maintenance just before circuit failure occurs, a chip remaining lifetime estimation method is in demand. In this paper, we propose a framework for predicting the remaining lifetime of the chip, which can adapt to changes in the process and operating voltage. The framework tracks representative aging indicators through machine learning methods in order to predict the remaining lifetime of the chip. In addition, we also investigate the impact of changes of hyperparameters, such as training sample sizes, on prediction performance. Experimental results show that our framework achieves an average accuracy and precision of 97.3% and 97.2%, and our accuracy is 2.54% higher than the strategy of chip health level compared to a previous work.