Self-Monitoring, Analysis And Reporting Technology Research Articles

SMART technologies in older adult care: a scoping review and guide for caregivers

PurposeA wide gap exists between the innovation and development of self-monitoring, analysis and reporting technology (SMART) technologies and the actual adoption by older adults or those caring for them. This paper aims to increase awareness of available technologies and describes their suitability for older adults with different needs. SMART technologies are intelligent devices and systems that enable autonomous monitoring of their status, data analysis or direct feedback provision.Design/methodology/approachThis is a scoping review of SMART technologies used and marketed to older adults or for providing care.FindingsFive categories of SMART technologies were identified: (1) wearable technologies and smart tools of daily living; (2) noninvasive/unobtrusive technology (i.e. passive technologies monitoring the environment, health and behavior); (3) complex SMART systems; (4) interactive technologies; (5) assistive and rehabilitation devices. Technologies were then linked with needs related to everyday practical tasks (mainly applications supporting autonomous, independent living), social and emotional support, health monitoring/managing and compensatory assistance rehabilitation.Research limitations/implicationsWhen developing, testing or implementing technologies for older adults, researchers should clearly identify concrete needs these technologies help meet to underscore their usefulness.Practical implicationsOlder adults and caregivers should weigh the pros and cons of different technologies and consider the key needs of older adults before investing in any tech solution.Social implicationsSMART technologies meeting older adult needs help support both independent, autonomous life for as long as possible as well as aiding in the transition to assisted or institutionalized care.Originality/valueThis is the first review to explicitly link existing SMART technologies with the concrete needs of older adults, serving as a useful guide for both older adults and caregivers in terms of available technology solutions.

Examining the impact of critical attributes on hard drive failure times: Multi‐state models for left‐truncated and right‐censored semi‐competing risks data

AbstractThe ability to predict failures in hard disk drives (HDDs) is a major objective of HDD manufacturers since avoiding unexpected failures may prevent data loss, improve service reliability, and reduce data center downtime. Most HDDs are equipped with a threshold‐based monitoring system named self‐monitoring, analysis and reporting technology (SMART). The system collects several performance metrics, called SMART attributes, and detects anomalies that may indicate incipient failures. SMART works as a nascent failure detection method and does not estimate the HDDs' remaining useful life. We define critical attributes and critical states for hard drives using SMART attributes and fit multi‐state models to the resulting semi‐competing risks data. The multi‐state models provide a coherent and novel way to model the failure time of a hard drive and allow us to examine the impact of critical attributes on the failure time of a hard drive. We derive dynamic predictions of conditional survival probabilities, which are adaptive to the state of the drive. Using a dataset of HDDs equipped with SMART, we find that drives are more likely to fail after entering critical states. We evaluate the predictive accuracy of the proposed models with a case study of HDDs equipped with SMART, using the time‐dependent area under the receiver operating characteristic curve (AUC) and the expected prediction error (PE). The results suggest that accounting for changes in the critical attributes improves the accuracy of dynamic predictions.

Failure Prediction of Hard Disk Drives in Redundant Arrays Using Disk-Level Performance Metrics

Although many hard drive failure prediction methods utilize Self-Monitoring Analysis and Reporting Technology (SMART) features, they are not collected in IT systems with demanding performance requirements to meet their specification. We present a novel data-driven method for the prediction utilizing disk-level performance metrics collected by Redundant Array of Independent Disk (RAID) controllers instead of SMART features. The proposed method computes relational anomaly scores leveraging logical relationships of Hard Disk Drives (HDDs) based on RAID configuration for better failure prediction. In addition, it further utilizes error codes from HDDs to filter out false positives. We evaluate the proposed method on a real-world dataset collected for this study from 881 disks used in disk arrays of RAID-6 and 1660 disks used in disk arrays of RAID-10 in a data center. The results show consistent performance improvement by the logical relationships and error-code-based filtering. In addition, seven out of nine failures are predicted one day before the failure at the latest. This result suggests that the proposed method provides plenty of time for HDD replacement before a failure occurs.

Hard Disk Failure Prediction Using Machine Learning

Abstract: 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.

A Survey Paper on Hard Disk Failure Prediction Using Machine Learning

Abstract: 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.

Predicting severely imbalanced data disk drive failures with machine learning models

Datasets related to hard drive failure, particularly BackBlaze Hard Drive Data, have been widely studied in the literature using many statistical, machine learning, and deep learning techniques. These datasets are severely imbalanced due to the presence of a small number of failed drives compared to huge amounts of healthy drives in the operational data centers. It is challenging to mitigate the adverse consequence of the class imbalance due to the presence of bias towards the majority class during learning. SMART (self monitoring analysis and reporting technology) attributes of the disk drives were utilized in the past to design standard classification or regression algorithms. Although few machine learning (ML) models, for instance, tree based methods and ensemble learning algorithms, addressed the failure prediction, the effects of class imbalance were rarely properly considered under the ML framework. This study, based on a review of the state-of-the-art in the area, evaluates current methodologies to identify areas that were either overlooked or lacking, proposes methods for remediating these issues, and performs some baseline experiments to demonstrate the proposed methodologies including data sampling techniques and cost-sensitive learning.

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (self-monitoring, analysis, and reporting technology) hard drive metrics with other system metrics, such as central processing unit (CPU) utilisation. Therefore, we propose a combined system metrics approach for failure prediction based on artificial intelligence to improve reliability. We tested over 100 cloud servers’ data and four artificial intelligence algorithms: random forest, gradient boosting, long short-term memory, and gated recurrent unit, and also performed correlation analysis. Our correlation analysis sheds light on the relationships that exist between system metrics and failure, and the experimental results demonstrate the advantages of combining system metrics, outperforming the state-of-the-art.

Significant capabilities of SMART sensor technology and their applications for Industry 4.0 in trauma and orthopaedics

The Industry 4.0 revolution has made it possible for the manufacturing of newly customised implants, tools, devices instruments for the medical field. Sensor technology uses embedded or surface devices that detect physical, chemical, biological signals and provide a way of interpretation of these signals to be measured and recorded. Sensor technology products have been established in various branches of medicine. In healthcare these applications have been utilized in the diagnosis, treatment and monitoring of diseases. Innovations in computer and design technology have allowed researchers to expand sensor technology in the field of orthopaedic surgery. SMART (Self- Monitoring Analysis and Reporting Technology) implants along with Sensor technology have been developed to provide improved diagnostic and therapeutic functions in the management of a spectrum of musculoskeletal conditions. SMART implants have evolved due to the advances in microchip technologies. In this article we explore the role of Sensor technology in the branch of trauma and orthopaedics. Experimental studies, in-vivo and current clinical applications of Sensor technology in total hip replacement, total knee replacement, spine surgery, fracture healing assessment is highlighted. Overall the significant capabilities of Sensor technology can be utilized to improve patient satisfaction and functional scores whilst managing their orthopaedic conditions.

Multi-Instance Adversarial Learning Domain Adaptation Network for Failure Prediction of Unlabeled Solid State Drives

Recently, with the large-scale application of solid state drives (SSDs), the failure of SSDs has become the main reliability issue in the data centers. SSDs manufacturers developed self-monitoring, analysis, and reporting technology (SMART) to indicate the health status of SSDs. However, for the newly enabled and the new types of SSDs in the data centers, lacking enough labeled data will be a critical problem, making failure prediction for SSDs a difficult task. To this end, this paper proposes a multi-instance adversarial learning domain adaptation network for coping with this task. A multi-instance learning method with an attention mechanism is designed to solve the problem of features extraction from unlabeled data by assigning weights to features over the lifespan. Moreover, the distribution differences between different SSDs models prevent the knowledge of the labeled information from being used for failure prediction of the unlabeled data, an adversarial domain adaptation method is used to align the distributions. Finally, the proposed method is verified on Alibaba’s dataset and shows much better performance than other methods.

Methodology for Management of the Protection System of Smart Power Supply Networks in the Context of Cyberattacks

This paper examines an approach that allows one to build an efficient system for protecting the information resources of smart power supply networks from cyberattacks based on the use of graph models and artificial neural networks. The possibility of a joint application of graphs, describing the features for the functioning of the protection system of smart power supply networks, and artificial neural in order to predict and detect cyberattacks is considered. The novelty of the obtained results lies in the fact that, on the basis of experimental studies, a methodology for managing the protection system of smart power supply networks in conditions of cyberattacks is substantiated. It is based on the specification of the protection system by using flat graphs and implementing a neural network with long short-term memory, which makes it possible to predict with a high degree of accuracy and fairly quickly the impact of cyberattacks. The issues of software implementation of the proposed approach are considered. The experimental results obtained using the generated dataset confirm the efficiency of the developed methodology. It is shown that the proposed methodology demonstrates up to a 30% gain in time for detecting cyberattacks in comparison with known solutions. As a result, the survivability of the Self-monitoring, Analysis and Reporting technology (SMART) grid (SG) fragment under consideration increased from 0.62 to 0.95.

Anomaly Detection Model for Predicting Hard Disk Drive Failures

ABSTRACT The electromechanical design of the HDD (Hard Disk Drive) renders it more susceptible to failures than other components of the computer system. The failure of HDD leads to permanent data loss, which is typically more expensive than HDD itself. The SMART (Self-Monitoring, Analysis and Reporting Technology) system warns the user if any HDD parameter has exceeded the predefined threshold value needed for safe HDD operation. Machine learning methods take advantage of dependence between multiple SMART parameters in order to make failure prediction more precise. In this paper, we present a failure prediction model based on the anomaly detection method involving an adjustable decision boundary. SMART parameters are ranked by the importance and the 13 most significant ones are used as the initial feature set in our model. In the following stage, we optimized the feature set by removing those that have no major contribution to the anomaly detection model, forming the final feature set comprising seven features only. The proposed anomaly detection model achieved 96.11% failure detection rate on average, with 0% false detection rate in ten random tests. The proposed model predicted more than 80% of failures 24 hours before their actual occurrence, which enables timely data backup.

Reliability Characterization and Failure Prediction of 3D TLC SSDs in Large-Scale Storage Systems

3D triple-level cell (TLC) NAND flash based solid state drive (SSD) is gradually becoming the dominant storage media in large-scale storage systems due to high storage density and low cost-per-bit. It ranks one of the top replaced hardware components in systems and their enormous amount also indirectly increases the failure probability, resulting in irreversible data loss disaster and service unavailability. This paper for the first time investigates system-level 3D TLC SSDs to characterize reliability and sub-health status based on field Self-Monitoring, Analysis and Reporting Technology (SMART) data, and predict impending failure proactively. We explore real-world datasets and derive some findings for each selected attribute in predetermined categories, contributing to the following feature selection and enhancing the interpretability of prediction models. Moreover, various machine learning models are trained to predict failures ahead of time, and experimental results show that random forest model can achieve 0.636 f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score and 0.662 MCC for a 7-day prediction horizon, and 42.5% true positive rate (TPR) with 0.00% false positive rate (FPR). Different time window sizes, training set fractions and ratios of negative to positive are analyzed as well.

Multi-Instance Deep Learning Based on Attention Mechanism for Failure Prediction of Unlabeled Hard Disk Drives

Failure of hard disk drives (HDDs) is the most critical reliability issue of data center. Therefore, predicting the failure of the HDD is an important means to ensure the storage security of the data center. However, most current research works had not paid attention to the fact that the self-monitoring, analysis and reporting technology (SMART) data in a returned failed HDD are a long-term sequence that consists of many unlabeled data, as the healthy and faulty data are highly mixed. Because the failure data in the rapid degradation period are less than the health data in the normal state, the mixture of healthy and faulty data results in an extremely data imbalance. This brings a great challenge to find the hidden fault information, and thus failure prediction becomes a difficult task. To cope with the above problems, a multi-instance long-term data classification method based on long short-term memory (LSTM) network and attention mechanism are proposed to predict the failure of HDDs. Regarding long time sequence HDD data as an instance bag, multi-instance learning (MIL) divides it into multiple instances in the subconcept layer, and then studies the connection between instances and bag labels. Based on the analysis of HDD data of a communication company and Backblaze data center, our proposed method can obtain much better results than other methods.

Deep Learning for HDD Health Assessment: An Application Based on LSTM

Hard disk drive failures are one of the most common causes of service downtime in data centers. Predictive maintenance techniques have been adopted to extend the Remaining Useful Life (RUL) of these drives, and minimize service shortage and data loss. Several approaches based on machine and deep learning techniques have been proposed to address these issues, mostly exploiting models based on Self-Monitoring analysis and Reporting Technology (SMART) attributes. While these models have proven to be reliable, their performance is affected by the lack of information about the proximity of disk failure in time. Moreover, many of these techniques are sensitive to the highly unbalanced nature of existing data-sets, in terms of good to failed hard disk ratio. In this article, we propose a LSTM (Long Short Term Memory)-based model combining SMART attributes and temporal analysis for estimating a hard drive health status according to its time to failure. Our approach outperforms state-of-the-art methods when evaluated on two data-sets, one containing hourly samples from 23395 disks and the other reporting daily samples from 29878‬ disks. Experimental results showed that our approach is well suited to data-sets with different sampling periods, being able to predict hard drive health status up to 45 days before failure.

Commercial hard drive failures in a data center application and the role of SMART attribute information

PurposeThe reliability of hard disk drives (HDDs) is dependent on the drive construction, as well as the operational and environmental conditions, in which the drive is used. Self-monitoring, analysis and reporting technology (SMART) continuously provides attribute information on HDD usage and degradation characteristics.Design/methodology/approachThis paper aims to analyze the reported failures Backblaze data set for ST3000DM001 HDDs intended for desktop applications within a data center application. SMART attributes used for predicting failure are discussed and analyzed over the life of many hard drives. A case study on the actual use of SMART and the limitations of the SMART attribute information, the data center’s information and the use of desktop drives in a commercial application are also presented.FindingsThe analysis showed that when Backblaze started to record the data, the hard disk drives had already worked for a while with power on hours mean and standard deviation of 6,683 and 365 h, respectively. Therefore, it is possible that some SMART attributes have experienced critical values that have not been recorded by Backblaze. Additionally, 8% of all ST3000DM001 drives that Backblaze labeled as failed did not have raw values above zero for the five attributes that were considered critical. Backblaze recorded 25 SMART attributes in total for all hard disk drive brands where ST3000DM001 having 83.3% of the attributes ranked as the drive with the most attributes recorded. Having more recorded attributes with critical values leads to label more ST3000DM001 drives as failed while there might be the hard drives from the other brands or part numbers that experienced more critical SMART attributes but were not labeled as failed because of the lack of records.Originality/valueIt is an original work carried out at the Center for Advanced Life Cycle Engineering, University of Maryland.

Evaluating Feature Selection and Anomaly Detection Methods of Hard Drive Failure Prediction

As vast amounts of data are saved, hard drive failure prediction is critical to reducing the cost of data loss and backup. Most existing studies used to detect the anomalous status of a hard drive using self-monitoring, analysis, and reporting technology (SMART) attributes, and then predicted whether the drive would have an impending failure. However, as most researchers focus on a specific model of hard drives, existing studies do not apply to different models due to the model-to-model variations. Furthermore, as anomaly detection algorithms were the focus in the literature, feature selection methods were less compared. This article proposes an evaluation methodology to compare feature selection methods and anomaly detection algorithms for hard drive failure prediction. It can quickly select the optimal algorithms for a specific model of drives. It contains an evaluation mechanism to assess feature selection methods from the perspectives of performance and robustness and assess the performance, the robustness, the efficiency, and the generalization of anomaly detection algorithms. Experiments on two data sets are implemented for validation, and the proposed method can achieve better performance than the existing approaches in the literature.

Feature Selection for Improving Failure Detection in Hard Disk Drives Using a Genetic Algorithm and Significance Scores

Hard disk drives (HDD) are used for data storage in personal computing platforms as well as commercial datacenters. An abrupt failure of these devices may result in an irreversible loss of critical data. Most HDD use self-monitoring, analysis, and reporting technology (SMART), and record different performance parameters to assess their own health. However, not all SMART attributes are effective at detecting a failing HDD. In this paper, a two-tier approach is presented to select the most effective precursors for a failing HDD. In the first tier, a genetic algorithm (GA) is used to select a subset of SMART attributes that lead to easily distinguishable and well clustered feature vectors in the selected subset. The GA finds the optimal feature subset by evaluating only combinations of SMART attributes, while ignoring their individual fitness. A second tier is proposed to filter the features selected using the GA by evaluating each feature independently, using a significance score that measures the statistical contribution of a feature towards disk failures. The resultant subset of selected SMART attributes is used to train a generative classifier, the naïve Bayes classifier. The proposed method is tested on a SMART dataset from a commercial datacenter, and the results are compared with state-of-the-art methods, indicating that the proposed method has a better failure detection rate and a reasonable false alarm rate. It uses fewer SMART attributes, which reduces the required training time for the classifier and does not require tuning any parameters or thresholds.

Adaptive fault-tolerant model for improving cloud computing performance using artificial neural network

Cloud computing is a paradigm that has greatly contributed to the emergence of new applications of IT services. However, the design of dependable and efficient cloud architectures requires a high knowledge of the recurring failures causes which compromise the efficient functioning of the system. Nowadays, the number of datasets recording the real state of cloud systems still rarely available to the public. This article uses failure data published by Backblaze which is one of the largest data storage providers. This dataset consists of the operating status of heterogeneous servers that were collected during the period between January 2015 and December 2018. Then, the data has been filtered and preprocessed to keep a total of 2,878,440 records including 128,820 failures. We investigate the correlation between hard drive parameters and failure ​​by exploiting the attributes of Self-Monitoring, Analysis and Reporting Technology (SMART). Then, we investigate the predictive capabilities of five machine learning models including naïve Bayes and artificial neural networks (ANN) to define a failure prediction module for the cloud architecture. The experimental results demonstrate that the artificial neural network (ANN) model offers the best prediction accuracy.

(Invited) Progress in Gradient Cathode Materials for High-Performance Rechargeable Lithium-Ion Batteries

High-energy-density rechargeable batteries are needed to fulfill various demands such as self-monitoring analysis and reporting technology (SMART) devices, energy storage systems, and (hybrid) electric vehicles. As a result, high-energy electrode materials enabling a long cycle life and reliable safety need to be developed. To ensure these requirements, new material chemistries can be derived from combinations of at least two compounds in a secondary particle with varying chemical composition and primary particle morphologies having a core−shell structure and spherical cathode-active materials, specifically a nanoparticle core and shell, nanoparticle core and nanorod shell, and nanorod core and shell. To this end, several layer gradient cathode materials were developed to ensure high capacity, reliability, and safety. One of the most promising oxides is full concentration gradient (FCG) lithium nickel-cobalt-manganese oxide composed of a Mn-rich outer surface providing excellent safety and Ni-rich center achieving high capacity. In addition, we extended the FCG concept and report a new novel Li[NixCoyMnz]O2 cathode material with two-sloped full concentration gradients (TSFCG) of Ni, Co, and Mn ions throughout the cathode particles to maximize the average Ni concentration at the core as active redox species and the Mn concentration in the area near the particle surface. Comparison of electrochemical and thermal properties of the TSFCG with those of NCA and conventional cathode Li[NixCoyMnz]O2 is presented.

Moving towards Real-time Data-driven Quality Monitoring: A Case Study of Hard Disk Drives

Since its emergence, the cloud manufacturing concept has been transforming the manufacturing and remanufacturing industry into a big data and service-oriented environment. The aggressive push toward data collection in cloud-based and cyber-physical systems provides both challenges and opportunities for predictive analytics. One of the key applications of predictive analytics in such domains is predictive quality management that aims to fully exploit the potentials provided by the enormous data collected via cloud-based systems. As a case study, a data set of hard disk drives’ Self-Monitoring, Analysis and Reporting Technology (SMART) attributes from a cloud-storage service provider has been analyzed to derive some insights about the challenges and opportunities of using product lifecycle data. An analysis of time-to-failure monitoring of hard disk drives in real-time has been carried out and the corresponding challenges have been discussed.