scholarly journals Hard disk drive failure detection with recurrence quantification analysis

2020 ◽  
Author(s):  
Wei Li
Keyword(s):  
Hard Disk Drive ◽  
Hard Disk ◽  
Failure Detection ◽  
Disk Drive ◽  
Recurrence Quantification Analysis ◽  
Recurrence Quantification ◽  
Quantification Analysis

scholarly journals Hard Disk Drive Failure Detection Using Hybrid Algorithm

2021 ◽  
Vol 9 (9) ◽  
pp. 1233-1244
Author(s):  
Vaibhav Umesh Mokal
Keyword(s):  
Logistic Regression ◽  
Hard Disk Drive ◽  
Learning Strategies ◽  
Data Centers ◽  
Naive Bayes ◽  
Hard Disk ◽  
Failure Detection ◽  
Disk Drive ◽  
Naïve Bayes ◽  
Modern World

Abstract: The data is the most valuable thing in this modern world of Information Technology. As we can see the day to day the data is increasing as each and every people using the World Wide Web. This all system generated data or may be the personal or informative data will get generated in a huge amount of size. That data will get stored at the data centers or on cloud. But those will get stored on the Hard Disk Drives in data centers. So in some situation if the HDD got crashed then we will have lost our data. This work proposes to develop the failure prediction of Hard disk drive. We have chosen the accuracy and review measurements, generally important to the issue, and tried a few learning strategies, Adaboost, Naive Bayes, Logistic Regression and Voting. Our investigation shows that while we can't accomplish close to 100% forecast precision utilizing ML with the present information we have accessible for HDDs, we can improve our expectation exactness over the standard methodology Keywords: Machine learning, Adaboost, Naive Bayes, Voting, Logistic Regression

scholarly journals Process Failure Detection via Recurrence Quantification Analysis in a Slot-Rectangular Spouted Bed

2020 ◽  
Vol 142 (7) ◽  
Author(s):  
Steven L. Rowan ◽  
Ronald W. Breault ◽  
Justin M. Weber ◽  
Narasimhan Soundarrajan
Keyword(s):  
Failure Modes ◽  
Time History ◽  
Side Wall ◽  
Failure Detection ◽  
Recurrence Quantification Analysis ◽  
Spouted Bed ◽  
Recurrence Quantification ◽  
Process Failure ◽  
Quantification Analysis ◽  
Drop Time

Abstract A study was conducted to explore the applicability of recurrence and recurrence quantification analysis (RQA) to the detection of process failures in spouted bed reactor systems. Three different potential failure modes were examined in a transparent, cold flow slot-rectangular spouted bed. These being, a simulated air leak from a side wall of the reactor, a simulated gas leak from the top wall of the reactor, and simulated agglomeration of solids via introduction of larger “klinker” particles. Bed pressure drop time history data were collected and analyzed via generation of recurrence plots (RPs) and RQA parameters. In general, the simulated agglomeration case was quite easily detected via ever RQA Parameter examined, whereas the simulated air leaks were detected by only a single RQA parameter.

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