Device Health Management Based on Machine Learning

Battery life is a crucial factor in assessing the longevity of electronic devices and machines. To enhance the management of machine health, we propose a neural network-based method for predicting battery life in machine learning. Neural networks,a key aspect of machine learning, simulate the human brain's neural network to improve existing performance. Battery capacity,a visual indicator of battery life, is essential for the safe operation of machines. In this study, we predict battery capacity to estimate remaining battery life. We use a NASA-provided lithiumbattery dataset containing time-series data from multiple sensors and labeling information related to battery state. These sensors measure various battery parameters, such as voltage. Our method, implemented using the MATLAB neural network toolbox, provides a visual display of the predicted battery decay curve, aiding industrial production judgment and algorithm verification. Additionally, our approach, developed for a specific battery device, has the potential for broader application in other electronic and electric devices.