A Time Series Classification Method for Battery Event Detection

The maintenance of batteries used in wireless mobile communication is an important practical problem. The experts can easily recognize the battery events, such as turning on, by watching the monitoring data. However it is infeasible to have experts watch the data all the time. There are devices that can report battery events. These devices sometimes report incorrect event. In order to solve this problem, we propose a time series classification framework to use the expert knowledge to build an accurate classifier, and then use the classifier to monitor the batteries in real time. We first propose an active learning method to efficiently collect the experts' labels for each event. Then we apply various feature extraction methods to convert each time series segment into a feature vector. Finally, we apply random forest classifier to perform the classification. Moreover, in practice, the labeled data is unbalanced, i.e. >99% of the data instances belong to a single label. We use bootstrap to solve this problem. We test our method on a dataset for 500 batteries in 3 months. The results show that our method achieves a very high classification accuracy, using only less than 1% of the dataset as training set.