Abstract: A method for operating a storage device and the storage device are provided. The method includes obtaining an initial failure warning model comprising a plurality of decision trees, the obtaining the initial failure warning model including being trained with a random forest algorithm based on historical failure related data, the historical failure related data comprising historical multiple operational attributes data of the storage device and failure logs of the storage device, determining high frequency decision nodes in the plurality of decision trees, the high frequency decision nodes being similar single-nodes with at least a first number in the plurality of decision trees, the similar single-nodes being single-nodes having a same monitoring attribute, a same attribute determination symbol, and a difference between attribute thresholds within a first range, and constructing the failure warning model of a new decision tree comprising the high frequency decision nodes.

Abstract: A failure prediction method and device for a storage device are provided. The method comprises: inputting SMART data of the storage device obtained in real time into each of a plurality of base classification models to obtain a classification result for the SMART data of the storage device obtained in real time that is output by the each classification model, wherein the each base classification model is obtained by training using historical SMART data of a plurality of storage devices and/or SMART data of the plurality of storage devices obtained online; determining whether the SMART data of the storage device obtained in real time is healthy data or erroneous data, based on classification results of the plurality of base classification models; predicting whether the storage device will fail, based on a number of SMART data that is determined as healthy data and a number of SMART data that is determined as erroneous data among SMART data of the storage device obtained within a predetermined time window.

Abstract: A failure prediction apparatus and method for storage devices are provided, the method including: obtaining attribute information of a plurality of attributes for a plurality of storage devices during operation of a storage apparatus; obtaining global attribute information for each of the plurality of storage devices based on the attribute information of the plurality of attributes obtained within a first time window before the current time; and predicting failures for the plurality of storage devices using a trained machine-learning model based on attribute information of the plurality of attributes of each of the plurality of storage devices obtained within a second time window before the current time and the global attribute information of each of the plurality of storage devices.

Abstract: A failure prediction method and device for a storage device are provided. The method comprises: inputting SMART data of the storage device obtained in real time into each of a plurality of base classification models to obtain a classification result for the SMART data of the storage device obtained in real time that is output by the each classification model, wherein the each base classification model is obtained by training using historical SMART data of a plurality of storage devices and/or SMART data of the plurality of storage devices obtained online; determining whether the SMART data of the storage device obtained in real time is healthy data or erroneous data, based on classification results of the plurality of base classification models; predicting whether the storage device will fail, based on a number of SMART data that is determined as healthy data and a number of SMART data that is determined as erroneous data among SMART data of the storage device obtained within a predetermined time window.