MLP-LightGBM Hard Drive Failure Prediction with Transfer Learning Performance

Hard drive failures persist in data centers, leading to data loss. To address these failures and enhance storage capacity, new hard drives are introduced to replace the failed ones. Therefore, data centers host hard drives from various suppliers, as well as different models from the same supplier. In the face of new hard drives, traditional models fail to deliver satisfactory prediction results. By estimating the remaining useful life(RUL) of a hard drive, operators can predict when a hard drive will fail and replace it at the optimal time, ensuring maximum utilization and reducing operating costs. Previous variant models based on LSTM and Transformer are slow to train, consume significant resources, and exhibit high RMSE. Therefore, this paper proposes a new MLP-LightGBM model for hard drive failure prediction with transfer learning performance, which not only achieves RUL prediction but also performs RUL prediction on new hard drives. The model consists of Time-processing and LightGBM. We embed the time series features of the hard drives into feature columns through a series of operations, including normalization, flipping, and MLP learning on the hard drive data. Subsequently, we use LightGBM to predict the RUL of the hard drives. We conducted experiments using ten years of datasets provided by the official Backblaze website, and the results indicate that our experimental outcomes are significantly superior to existing RUL prediction methods. For the first time, we apply transfer learning to predict RUL of hard drives and demonstrate that our model achieves accurate results in transfer learning.