  This paper presents a novel approach to fall prediction for bipedal robots,
specifically targeting the detection of potential falls while standing caused
by abrupt, incipient, and intermittent faults. Leveraging a 1D convolutional
neural network (CNN), our method aims to maximize lead time for fall prediction
while minimizing false positive rates. The proposed algorithm uniquely
integrates the detection of various fault types and estimates the lead time for
potential falls. Our contributions include the development of an algorithm
capable of detecting abrupt, incipient, and intermittent faults in full-sized
robots, its implementation using both simulation and hardware data for a
humanoid robot, and a method for estimating lead time. Evaluation metrics,
including false positive rate, lead time, and response time, demonstrate the
efficacy of our approach. Particularly, our model achieves impressive lead
times and response times across different fault scenarios with a false positive
rate of 0. The findings of this study hold significant implications for
enhancing the safety and reliability of bipedal robotic systems.
