About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
BigData Congress 2018
Conference paper
Large Scale Predictive Analytics for Hard Disk Remaining Useful Life Estimation
Abstract
Hard disk failure prediction plays an important role in reducing data center downtime and improving service reliability. In contrast to existing work of modeling the prediction problem as classification tasks, we aim to directly predict the remaining useful life (RUL) of hard disk drives. We experiment with two different types of machine learning methods: random forest and long short-term memory (LSTM) recurrent neural networks. The developed machine learning models are applied to predict RUL for a large number of hard disk drives. Preliminary experimental results indicate that random forest method using only the current snapshot of SMART attributes is comparable to or outperforms LSTM, which models historical temporal patterns of SMART sequences using a more sophisticated architecture.