Authors: Prof. R.T. Waghmode, Abdulmuiz Shaikh, Shreyash Bajhal, Harsh Dubey, Anuj Bhadoriya
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Failure of Hard Disk is a term most companies and people, fear about. People get concerned regarding data loss. Therefore, predicting the failure of the HDD is an important and to ensure the storage security of the data center. There exist a system named, S.M.A.R.T. (Self-Monitoring, Analysis and Reporting Technology) in hard disk tools or bios tools which stands for Self-Monitoring, Analysis and Reporting Technology. Our project will be predicting the failure of hard drive whether it will fail or not. This prediction will be based on Machine Learning algorithm. S.M.A.R.T. values of hard disk will be extracted from external tool.
HDD failure will not only cause the loss of data, but also may cause the entire storage and computing system to crash, resulting in immeasurable property loss to individuals or enterprises. Being able to detect in advance, an HDD failure may both prevent data losses from happening and reduce service downtime. There exist a system named, S.M.A.R.T. in hard disk tools or bios tools which stands for Self-Monitoring, Analysis and Reporting Technology. This method returns unlabelled data overtime, and the healthy and faulty data are highly mixed. This returned data will be fed to ML algorithm to predict the hard drive failure.
The project is intended for individual people who are looking for a life prediction of their hard drives.Output from the system will be Fail or Pass. The hard disk failure can cause data loss.Hence predicting failure a way ahead will help people avoid data loss. This can also be brought into the data centres where huge number of hard drives are present.This project can be scaled for data centres.This will avoid data loss and help data centre managers take necessary action before it fails.
III. LITERATURE SURVEY
HDD failure will not only cause the loss of data, but also may cause the entire storage and computing system to crash, resulting in immeasurable property loss to individuals orenterprises. Being able to detect in advance, an HDD failure may both prevent data losses from happening and reduce service downtime. There exist a system named, S.M.A.R.T. in hard disk tools or bios tools which stands for Self-Monitoring, Analysis and Reporting Technology. This method returns unlabelled data overtime, and the healthy and faulty data are highly mixed. This returned data will be fed to ML algorithm to predict the hard drive failure .
In the paper , Use of decision trees, The fault prediction model can handle the failed hard disk in advance data backup and migration timely, so as to avoid failure and data loss, to protect the data security in the data center. But it also says decision trees are largely unstable compared to other decision predictors, also, they are less effective in predicting the outcome of a continuous variable. In ,the author proposed use of Deep Recurrent Neural Networks (DRNN). DRNN was chosen because of its remarkable performance in many applications including HDDs failure prediction. The limitation of  was the computation of this neural
network is slow, training the model can be difficult task, also it faces issues like exploding or Gradient vanishing.
In the paper  author uses XGBoost, LSTM and ensemble learning algorithm to effectively predict disk faults. Also in this paper here,LSTM takes longer to train, requires more memory. XGBoost does not perform so well on sparse and unstructured data.
In ,  the dataset statistically used to discover failure characteristics along the temporal, spatial, product line and component dimensions. And specifically focus on the cor relations among different failures, including batch and repeating failures, as well as the human operators’ response to the failures.
The study  is based on empirical observation that reallocated sector count, a metric recorded by the disk drive, increases prior to failure. But it’s limitation is in empirical observations, calculations can be very expensive, and also shows lack of reliability.
Even if the experimental design process is shared by all study areas in some ways, ML tactics need to be cross-disciplinary. The ML technique's steps that are unique for hard disk failure prediction are as follows: The key five steps are data collection, Preparing the Data, Choosing a Model and Training the Model, Evaluating the Model and System Integration.
A. Data Collection
Collecting data for hard disk failure prediction typically involves monitoring the system performance using various sensors and diagnostic tools. These sensors can collect data on various parameters, such as temperature, humidity, vibration, and noise, that can provide insights into the health of the hard disk.
There are several ways to collect data for hard disk failure prediction, including
B. Preparing the Data
Preparing the data for hard disk failure prediction involves several important steps to ensure the data is in a suitable format for training and evaluating the prediction model. The following steps are commonly involved in preparing the data:
C. Choosing a Model and Training the Model
Choosing a suitable model and training it are crucial steps in hard disk failure prediction.Some important steps are:
D. Evaluating the Model
Evaluating the model in hard disk failure prediction is a critical step to assess its performance and reliability:
Cross-Validation: Employ cross-validation techniques to assess the model's generalization capability and robustness. K-fold cross-validation divides the dataset into k equally sized folds, with each fold serving as a testing set while the remaining folds are used for training. This process is repeated k times, and the average performance across all folds provides a more reliable estimate of the model's performance.
E. System Integration
System integration in hard disk failure prediction using Python and Tkinter involves incorporating Tkinter functionality to create a desktop application with a graphical user interface (GUI) for interacting with the prediction system.
In the case of Bagging with Decision Trees, multiple decision tree models are trained using bootstrap sampling. Bootstrap sampling involves randomly selecting subsets of the original dataset with replacement, creating new training sets for each decision tree. This process allows each tree to be trained on slightly different data, introducing diversity in the models.
During training, each decision tree is grown by recursively splitting the data based on different features and thresholds. The trees can be deep or shallow, depending on the complexity of the problem and the desired level of generalization. Each tree independently makes predictions based on its internal structure and the majority vote (in classification) or average (in regression) of the predictions from all trees is used as the final prediction.
VI. FUTURE SCOPE
With the proliferation of cloud computing and remote monitoring capabilities, hard disk failure prediction systems can be integrated into cloud-based platforms. This enables centralized monitoring and analysis of large-scale disk arrays, providing insights into the health and failure risks of multiple hard disks simultaneously.
Developing real-time monitoring systems that continuously collect and analyze hard disk data can provide timely alerts and notifications when the risk of failure increases. This allows for immediate action to be taken, such as data backup, migration, or replacement, to minimize the impact of potential failures.
Instead of relying solely on SMART data, the fusion of multiple data sources and modalities can provide a comprehensive view of the hard disk's health. Incorporating additional sensor data, such as temperature, vibration, or acoustic signals, along with SMART attributes, can improve the predictive capabilities and enable more robust failure detection.
The literature survey summarizes previous works, most of the work was based on neural network strategies. These were expensive methods with all their respective limitation. Some work was lacking accuracy, where some were using out dated software tools. Some work were using much time and memory resources. Hence this summarizes the literature survey.
 Jian Zhao, Yongzhan He, Hongmei Liu, Jiajun Zhang, Bin Liu “Disk Failure Early Warning Based on the Characteristics of Customized SMART”, In 2020  Fernando D. S. Lima, Francisco Lucas F. Pereia, Lago C. Chaves “Predicting the Health Degree of Hard Disk Drives with Asymmetric and Ordinal Deep Neural Models” , In 2020  Qiang Li and Hui Li, Kai Zhang “Prediction of HDD Failures by Ensemble Learning” In 2019  Guosai Wang, Wei Xu, Lifei Zhang “What Can We Learn from Four Years of Data Center Hardware Failures?”, In 2017  Paul H. Franklin, Primus software “Predicting Disk Drive Failure Using Condition Based Monitoring” ,In 2017  Lucas P. Queiroz, Francisco Caio M. Rodrigues, Joao Paulo P. Gomes, Felip T. Brito “A Fault Detection Method for Hard Disk Drives Based on Mixture of Gaussian and Non-Parametric Statistics” ,In 2016  Iago C. Chaves, Manoel Rui P. de Paula, Lucas G. M. Leite, Lucas P. Queiroz, Joao Paulo P. Gomes, Javam C. Machado “BaNHFaP: Hard Disk Failure Prediction Using Machine learning A Bayesian Network based Failure Prediction Approach for Hard Disk Drives”, In 2016  Jing Li, Xinpu Ji, Yuhan Jia, Bingpeng Zhu, Gang Wang “Hard Drive Failure Prediction Using Classification and Regression Trees” , In 2014  Chang Xu, Gang Wang, Xiaoguang Liu, Dongdong Guo, and Tie-Yan Liu “Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks”, In 2014  Yu wang, Eden W. M. Ma, Tommy W. S. Chow and Kwog-Leung Tsui “A Two-Step Parametric Method for Failure Prediction in Hard Disk Drives” , In 2014
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