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.

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.

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.