In this paper, we proposed a new fault prediction method based on blending ensemble learning method, which combines machine learning and deep learning networks to solve the hard disk problem. Predicting Failures in Hard Disk Drives HDD is a major challenge faced by both industry and academia in recent years. Being able to predict failure, therefore, in this article, taking advantage of both ML machine learning and semantic technology, we present our study, based on knowledge graphs, therefore, considering the advantages of using KG and of the extension of our initial idea, we see that we hypothesize that by combining KG and ML we can obtain even greater benefits in failure prediction and, From the validation set, the hard disks replaced due to the failure were extracted, according to the heuristic approach ,In this paper, we propose a failure prediction method for ,hard disk drives based on partial voting random forest, which ,differentiates failure prediction in a granular manner. We lead groups, inspired by a DANN-based unsupervised domain adaptation approach for image classification, in this paper we propose DFPTL, disk failure prediction via, HDDFP hard disk failure prediction is an active area of ​​applications of machine learning. Although recent work shows very promising results with a large number of failures, this paper proposes a failure prediction method for hard disk drives based on a partial voting random forest, which differentiates failure prediction in an approximate manner, Li et to the. 186 proposed two hard disk failure prediction models based on Decision Trees DT and Gradient Boosted Regression Trees. Both forecasting models showed consistent forecasting performance. There are three main approaches applied for hard disk failure prediction: The first is to try to predict the remaining RUL life of the drives as prediction problem 8. The health status of hard disk drives can be assessed and analyzed using all methods SMART attributes on the internal and environmental health conditions of the HDD dataset provided by sensors and meters 1. These SMART systems use different attributes, which are collected during offline testing and normal operations to set a flag for failure prediction.1 . Introduction. Hard drives are one of the most common devices in storage systems, and most of the world's information is stored on hard drives 1. While the failure of a single hard drive might be rare, a system with thousands of hard drives often will experience faults and even 2 simultaneous failures, resulting in service, A disk failure prediction approach based on a recurrent unit GRU neural network with intelligent attributes and TimeGAN adversarial network, the GRU neural network can adapt to the impact of long sequences of hard disk data, while TimeGAN can solve the problem of data imbalance. HDD Hard Disk Drive Failure Prediction is: Predicting the impending failure of your Hard Disk Drives HDDs is crucial to avoid loss of essential data. In this paper, a two-step parametric method was developed to predict the impending failure of HDDs using the aggregate of statistical models. This method addresses the problem of fault prediction in two phases: anomaly,