SmartPred: Unsupervised Hard Disk Failure Detection MLHPCS 2020 - PowerPoint PPT Presentation

Apr 04, 2024 •264 likes •413 views

SmartPred: Unsupervised Hard Disk Failure Detection MLHPCS 2020 PHILIPP ROMBACH & JANIS KEUPER 2 Hard Disk Drive Isolation Forest SmartPred Dataset Agenda Feature Selection Setup Evaluation Replacement

SmartPred: Unsupervised Hard Disk Failure Detection MLHPCS 2020 PHILIPP ROMBACH & JANIS KEUPER
2  Hard Disk Drive  Isolation Forest  SmartPred  Dataset Agenda  Feature Selection  Setup  Evaluation  Replacement Process
3 Hard Disk Drive Structure Failures  Disk  Mechanical  Electrical  Controller Firmware
4 Hard Disk Drive - SMART Attribute Definition Attribute Definition SMART 1 Read Error Rate SMART 190 Airflow Temperature SMART 3 Spin Up Time SMART 191 G-sense Error Rate SMART 4 Start/Stop Count SMART 192 Power-off Retract Count SMART 5 Reallocated Sectors Count SMART 193 Load Cycle Count SMART 7 Seek Error Rate SMART 194 Temperature SMART 9 Power-On Hours SMART 197 Current Pending Sector Count SMART 10 Spin Retry Count SMART 198 Uncorrectable Sector Count SMART 12 Power Cycle Count SMART 199 UltraDMA CRC Error Count SMART 184 End-to-End error / IOEDC SMART 240 Head Flying Hours SMART 187 Reported Uncorrectable Errors SMART 241 Total LBAs Written SMART 188 Command Timeout SMART 242 Total LBAs Read
5 Isolation Forest Anomaly X 0 Normal Instance X n
6 SmartPred - Dataset date serial_number model capacity_bytes failure smart_5_normalized smart_5_raw 01.01.2019Z305B2QN ST4000DM000 4,00079E+12 0 119.0 0 01.01.2019ZJV0XJQ4 ST12000NM0007 1,20001E+13 0 82.0 85 01.01.2019ZJV0XJQ3 ST12000NM0007 1,20001E+13 0 80.0 255 01.01.2019ZJV0XJQ0 ST12000NM0007 1,20001E+13 1 78.0 1034 01.01.2019PL1331LAHG1S4H HGSTHMS5C4040ALE64 4,00079E+12 0 100.0 0  Samples: 12614746  Disks: 37768  Failed Disks: 868
7 SmartPred - HDD Operating Time
8 SmartPred - Feature Selection Correlation coefficient to feature Failure  SMART_5_raw: 0.07  SMART_187_raw: 0.02  SMART_197_raw: 0.03  SMART_5_diff: 0.12  SMART_187_diff: 0.01  SMART_197_diff: 0.03
9 SmartPred - Hyperparameter
10 SmartPred - Evaluation Precision Recall
11 SmartPred - Evaluation FDR FAR Precision Computation Time IF_FDR 84.54% 0.0073% 44.21% 1.10 s ± 10 ms IF_FAR 28.37% 0.0006% 77.85% 1.10 s ± 10 ms OneClassSVM 95.39% 0.2925% 2.19% 15.9 s ± 780 ms LocalOutlierFactor 96.19% 1.1916% 0.55% 16.0 s ± 671 ms
12 SmartPred - Replacement Process Node 1 Node 1 Node 2 Node 2
13 Thank you for watching!

Recommend

UNSUPERVISED LEARNING, CLUSTERING UNSUPERVISED LEARNING UNSUPERVISED LEARNING Supervised

UNSUPERVISED LEARNING, CLUSTERING UNSUPERVISED LEARNING UNSUPERVISED LEARNING Supervised learning: X - y pairs, f(x) function approximation Unsupervised learning: only X, no y Exploring the space of X measurements,

440 views • 14 slides

Introduction to PCA Unsupervised Learning in R Unsupervised learning Two methods of

UNSUPERVISED LEARNING IN R Introduction to PCA Unsupervised Learning in R Unsupervised learning Two methods of clustering finding groups of homogeneous items Next up, dimensionality reduction Find structure in features

904 views • 29 slides

On the Limitations of Unsupervised Bilingual Dictionary Induction Anders Sgaard Sebastian

On the Limitations of Unsupervised Bilingual Dictionary Induction Anders Sgaard Sebastian Ruder Ivan Vuli Background: Unsupervised MT 2 Background: Unsupervised MT Recently: Unsupervised neural machine translation (Artetxe

1.36k views • 98 slides

Unsupervised Learning and Clustering l In unsupervised learning you are given a data set with no

Unsupervised Learning and Clustering l In unsupervised learning you are given a data set with no output classifications (labels) l Clustering is an important type of unsupervised learning PCA was another type of unsupervised learning l The

858 views • 38 slides

Unsupervised Learning Unsupervised vs Supervised Learning: Most of this course focuses on

Unsupervised Learning Unsupervised vs Supervised Learning: Most of this course focuses on supervised learning methods such as regression and classification. In that setting we observe both a set of features X 1 , X 2 , . . . , X p for each

934 views • 50 slides

Unsupervised Learning Unsupervised vs Supervised Learning: Most of this course focuses on

721 views • 60 slides

Automatic Learning of a Morphological Model Theory and Unsupervised Approaches Unsupervised

Automatic Learning of a Morphological Model Theory and Unsupervised Approaches Unsupervised Learning - ToC Foundations Problem description General architecture Papers: Goldsmith 2001 and 2006 Goldsmith and Hu 2004

1.23k views • 43 slides

Unsupervised Learning Andrea Passerini passerini@disi.unitn.it Machine Learning Unsupervised

Unsupervised Learning Andrea Passerini passerini@disi.unitn.it Machine Learning Unsupervised Learning Unsupervised Learning Setting Supervised learning requires the availability of labelled examples Labelling examples can be an extremely

752 views • 27 slides

12. Unsupervised Deep Learning CS 535 Deep Learning, Winter 2018 Fuxin Li With materials from

12. Unsupervised Deep Learning CS 535 Deep Learning, Winter 2018 Fuxin Li With materials from Wanli Ouyang, Zsolt Kira, Lawrence Neal, Raymond Yeh, Junting Lou and Teck-Yian Lim Unsupervised Learning in General Unsupervised learning is

1.48k views • 115 slides

UNSUPERVISED LEARNING AND CLUSTERING Jeff Robble, Brian Renzenbrink, Doug Roberts Unsupervised

UNSUPERVISED LEARNING AND CLUSTERING Jeff Robble, Brian Renzenbrink, Doug Roberts Unsupervised Procedures A procedure that uses unlabeled data in its classification process. Why would we use these? Collecting and labeling large data sets can

544 views • 28 slides

Practical Unsupervised Learning INFO/CS 4300, Spring 2016 Jack Hessel Unsupervised Learning is

Practical Unsupervised Learning INFO/CS 4300, Spring 2016 Jack Hessel Unsupervised Learning is Cool! But how can we use this in our projects? 1. 2. But first, lets look at our dataset... Data Dimensionality! Probability of Lung Cancer

999 views • 55 slides

lti 1 (typically) Unsupervised learning in NLP non-convex optimization lti 2

Concavity and Initialization for Unsupervised Dependency Parsing Kevin Gimpel Noah A. Smith lti 1 (typically) Unsupervised learning in NLP non-convex optimization lti 2 Dependency Model with Valence (Klein & Manning,

718 views • 45 slides

Unsupervised learning introduction October 7, 2019 Unsupervised learning introduction

Unsupervised learning introduction October 7, 2019 Unsupervised learning introduction October 7, 2019 1 / 39 Intro General statement of the problem One has a set of N observations ( x 1 , x 2 , ..., x N ) of a random p -vector X having

495 views • 32 slides

Machine Learning for NLP Unsupervised Learning Aurlie Herbelot 2019 Centre for Mind/Brain

Machine Learning for NLP Unsupervised Learning Aurlie Herbelot 2019 Centre for Mind/Brain Sciences University of Trento 1 Unsupervised learning In unsupervised learning, we learn without training data. The idea is to find a

998 views • 67 slides

Unsupervised learning General introduction to unsupervised learning PCA Special directions

Unsupervised learning General introduction to unsupervised learning PCA Special directions These are special directions we will try to find. Best direction u : |u| 2 = 1 2 1. Minimize : d i X i T u is the projection length x i u d i T

1.01k views • 29 slides

Unsupervised Learning Introduction Nakul Verma Unsupervised Learning What can we learn from

Unsupervised Learning Introduction Nakul Verma Unsupervised Learning What can we learn from data when label information is not available? Supervised learning framework Supervised learning Data: Assumption: there is a (relatively simple)

442 views • 16 slides

Tagger Comparison (Gao, Johnson) John Wieting CS 598 Unsupervised POS tagging Predict the

Unsupervised HMM POS Tagger Comparison (Gao, Johnson) John Wieting CS 598 Unsupervised POS tagging Predict the tags for each word in a sentence 2 approaches used in this paper o Maximum likelihood o Bayesian Notice the prior which

997 views • 21 slides

Unsupervised Learning About this class Build a model for your data. Which datapoints

Unsupervised Learning About this class Build a model for your data. Which datapoints Unsupervised learning are similar? k -means Clustering Nowadays lots of work on using unlabeled data to improve the performance of supervised learn-

450 views • 6 slides

Unsupervised, Fast and Precise Recognition of Digital Arcs in Noisy Images T. P . NGUYEN, B.

Introduction Arc segmentation Unsupervised Noise Detection A framework for arc recognition along noisy curves Experimentations Conclusions and futur work Unsupervised, Fast and Precise Recognition of Digital Arcs in Noisy Images T. P .

550 views • 51 slides

4CSLL5 Parameter Estimation (Supervised and Unsupervised) Unsupervised Maximum Likelihood

4CSLL5 Parameter Estimation (Supervised and Unsupervised) 4CSLL5 Parameter Estimation (Supervised and Unsupervised) Outline 4CSLL5 Parameter Estimation (Supervised and Unsupervised) Unsupervised Maximum Likelihood (re-)Estimation Hidden

310 views • 8 slides

THE MAGIC OF UNSUPERVISED LEARNING Agustinus Nalwan Head of AI Carsales.com.au A LITTLE BIT

THE MAGIC OF UNSUPERVISED LEARNING Agustinus Nalwan Head of AI Carsales.com.au A LITTLE BIT ABOUT MYSELF AI 4 Years SAVING OUR FOREST BUSHFIRE DETECTION BACK TO THE MAIN TOPIC THE MAGIC OF UNSUPERVISED LEARNING SUPERVISED LEARNING

1.11k views • 82 slides

Unsupervised Learning Principal Component Analysis CMSC 422 M ARINE C ARPUAT marine@cs.umd.edu

Unsupervised Learning Principal Component Analysis CMSC 422 M ARINE C ARPUAT marine@cs.umd.edu Slides credit: Maria-Florina Balcan Unsupervised Learning Discovering hidden structure in data Last time: K-Means Clustering What is the

502 views • 15 slides

Unsupervised Learning of the Morphology of a Natural Language John Goldsmith University of

Unsupervised Learning of the Morphology of a Natural Language John Goldsmith University of Chicago Abstract This study reports the results of using Minimum Description Length analysis to model unsupervised learning of the morphology of

1.15k views • 101 slides

Unsupervised Learning Gustavo Velasco-Hern andez Pattern Recognition, 2014 Gustavo

Outline Introduction Clustering Feature Selection and Extraction Networks for Unsupervised Learning End Unsupervised Learning Gustavo Velasco-Hern andez Pattern Recognition, 2014 Gustavo Velasco-Hern andez Unsupervised Learning

912 views • 53 slides