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|a 1264722684
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|a 10.1007/978-3-030-79519-1
|2 doi
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|a (OCoLC)1265462438
|z (OCoLC)1264722684
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|a Smart monitoring of rotating machinery for Industry 4.0 /
|c Fakher Chaari, Xavier Chiementin, Radoslaw Zimroz, Fabrice Bolaers, Mohamed Haddad, editors.
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| 264 |
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|a Cham :
|b Springer,
|c [2022]
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|c ©2022
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| 300 |
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|a 1 online resource (177 pages) :
|b illustrations (chiefly color)
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|a text
|b txt
|2 rdacontent
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|a computer
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|2 rdamedia
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|a online resource
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1 |
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|a Applied condition monitoring ;
|v volume 19
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| 520 |
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|a This book offers an overview of current methods for the intelligent monitoring of rotating machines. It describes the foundations of smart monitoring, guiding readers to develop appropriate machine learning and statistical models for answering important challenges, such as the management and analysis of a large volume of data. It also discusses real-world case studies, highlighting some practical issues and proposing solutions to them. The book offers extensive information on research trends, and innovative strategies to solve emerging, practical issues. It addresses both academics and professionals dealing with condition monitoring, and mechanical and production engineering issues, in the era of industry 4.0.
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|a Print version record.
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|a Intro -- Contents -- Vulnerabilities and Fruits of Smart Monitoring -- 1 Introduction -- 1.1 The Ultimate System -- 1.2 What Is Smart Monitoring? -- 1.3 Smart Systems Versus Smart Staff -- 2 Evolution of Condition Monitoring Systems -- 2.1 Early Days -- 2.2 Expansion of Stationary Distributed Systems -- 2.3 Industrial Internet-of-Things -- 3 CMS Interaction with Human -- 3.1 Selection -- 3.2 Configuration -- 3.3 Operation -- 3.4 Maintenance Planning -- 4 Recommendations for Selection of Suitable System -- 5 Summary -- References -- A Tutorial on Canonical Variate Analysis for Diagnosis and Prognosis -- 1 Introduction -- 2 Canonical Variate Analysis for Diagnosis -- 2.1 The Basic Framework of CVA -- 2.2 Determination of the Number of Retained States -- 2.3 Determination of Fault Threshold -- 2.4 Extensions of CVA-Canonical Variate Dissimilarity Analysis -- 2.5 Industrial Case Study-Canonical Variate Analysis -- 3 Canonical Variate Analysis for Prognosis -- 3.1 CVA-Based State Space Models -- 3.2 Determining the Number of Retained States -- 3.3 Example of Using CVA State Space Model for Prognosis -- 3.4 CVA-Based Data Driven Models -- 4 Conclusion -- References -- A Structured Approach to Machine Learning Condition Monitoring -- 1 Introduction -- 2 Machine Learning -- 2.1 Deep Learning -- 2.2 Advantages and Drawbacks of the Machine Learning Supervised and Unsupervised Techniques in CBM -- 3 Development of Classifiers with Machine Learning Algorithms -- 4 Model Development Workflow -- 5 Conclusions -- References -- A Structured Approach to Machine Learning for Condition Monitoring: A Case Study -- 1 Introduction -- 2 Random Forest -- 3 Deep Learning/Autoencoder -- 4 Problem Description -- 4.1 Preliminary Test on Rotary Test Rig -- 4.2 XTS Test Rig -- 4.3 Autoencoder for Anomaly Detection -- 5 Conclusions -- References.
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8 |
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|a Dynamic Reliability Assessment of Structures and Machines Using the Probability Density Evolution Method -- 1 Introduction -- 2 The Probability Density Evolution Method -- 2.1 The PDEM Equation -- 2.2 Physical Interpretation of the PDEM -- 2.3 Dynamic Reliability Assessment Using PDEM -- 3 Dynamic Reliability Assessment of Structures -- 3.1 Offline PDEM-Based Reliability Assessment Method -- 3.2 Online PDEM-Based Reliability Assessment Method -- 3.3 Case Study: Cantilevered Beam -- 4 Dynamic Reliability Assessment of Machines -- 4.1 Extra Considerations for Dynamic Reliability Assessment of Machines -- 4.2 Case Study: Bearing -- 5 Discussion and Future Research Directions -- 5.1 Future Research Directions -- References -- Rotating Machinery Condition Monitoring Methods for Applications with Different Kinds of Available Prior Knowledge -- 1 Introduction -- 2 Prior Knowledge in Condition Monitoring -- 2.1 Engineering Knowledge -- 2.2 Knowledge Extracted from Machine Learning Algorithms -- 3 Case Study -- 3.1 Data Availability: Level 0 -- 3.2 Data Availability: Level 1 -- 3.3 Data Availability: Level 2 -- 4 Conclusions and Recommendations -- References -- Model Based Fault Diagnosis in Bevel Gearbox -- 1 Introduction -- 2 Dynamic Modelling of One Stage Straight Bevel Gearbox -- 3 Modelling of Mesh Stiffness Function -- 3.1 Mesh Stiffness Model of a Healthy Bevel Gear -- 3.2 Mesh Stiffness Model of Bevel Gear with a Missing Tooth Fault -- 4 Simulation and Results -- 4.1 Dynamic Response of a Healthy Bevel Gear System -- 4.2 Dynamic Response of a Bevel Gear System with Missing Tooth Fault -- 5 Experimental Validation -- 6 Conclusion -- References -- Investigating the Electro-mechanical Interaction Between Helicoidal Gears and an Asynchronous Geared Motor -- 1 Introduction -- 2 Experimental Set Up -- 3 Results -- 4 Conclusion -- References.
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8 |
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|a Algebraic Estimator of Damping Failure for Automotive Shock Absorber -- 1 Introduction -- 2 Vehicle Model -- 3 Proposed Algebraic Estimator -- 4 Results of Simulation -- 5 Conclusion -- References -- On the Use of Jerk for Condition Monitoring of Gearboxes in Non-stationary Operations -- 1 Introduction -- 2 Dynamic Model -- 3 Numerical Simulations -- 3.1 Stationary Operating Conditions -- 3.2 Non-stationary Operating Conditions -- 3.3 Influence of Noise -- 4 Conclusion -- References -- Dynamic Remaining Useful Life Estimation for a Shaft Bearings System -- 1 Introduction -- 2 Methodology -- 3 Validation of the Proposed Approach -- 3.1 Experimental Setup -- 3.2 Results and Discussion -- 4 Conclusion -- References.
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| 650 |
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|a Electric machinery
|x Monitoring.
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| 650 |
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0 |
|a Machine learning.
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| 650 |
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|a Industry 4.0.
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| 650 |
|
7 |
|a Electric machinery
|x Monitoring
|2 fast
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| 650 |
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7 |
|a Industry 4.0
|2 fast
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| 650 |
|
7 |
|a Machine learning
|2 fast
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| 700 |
1 |
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|a Chaari, Fakher,
|e editor.
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| 700 |
1 |
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|a Chiementin, Xavier,
|e editor.
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| 700 |
1 |
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|a Zimroz, Radosław,
|e editor.
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| 700 |
1 |
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|a Bolaers, Fabrice,
|e editor.
|
| 700 |
1 |
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|a Haddar, Mohamed,
|e editor.
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| 758 |
|
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|i has work:
|a Smart monitoring of rotating machinery for Industry 4.0 (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCG3FyYRTpKKRbvHMmcyBGd
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Chaari, Fakher.
|t Smart Monitoring of Rotating Machinery for Industry 4. 0.
|d Cham : Springer International Publishing AG, ©2021
|z 9783030795184
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| 830 |
|
0 |
|a Applied condition monitoring ;
|v volume 19.
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| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-79519-1
|y Click for online access
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| 903 |
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|a SPRING-ENGINE2022
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| 994 |
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|a 92
|b HCD
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