Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems: Novel Methods for Condition Monitoring and Diagnostics ... in Intelligent Systems and Computing, 1096)

Preface
Contents
About the Editors
A Conceptual Introduction and Review on Condition Monitoring and Diagnostics of Electrical and Mechanical Systems
Fault Tolerant Controller Schemes for Single and Multiple Mobile Robots
	1 Introduction
	2 Literature Review on Fault Detection and Isolation  of Mobile Robots
		2.1 Single Mobile Robot
		2.2 Multi-Mobile Robots
	3 Literature Review on Fault Tolerant of Mobile Robots
		3.1 Single Mobile Robot
		3.2 Multi-Mobile Robots
	4 Qbot-2 and Kinect
	5 FDI and FTC of Mobile Robots Using Joint State  and Parameter Estimation
		5.1 Kinematic Equation
		5.2 Joint State and Parameter Estimation—EKF
		5.3 Fault Tolerant Control of Single Mobile Robot in Trajectory Tracking Mission
		5.4 Fault Tolerant Control of Multiple Mobile Robot in Leader–Follower Mission
		5.5 Fault Tolerant Obstacle Avoidance Controller for Single Mobile Robot
	6 Conclusion and Future Works
	References
Faults and Fault Detection Methods in Electric Drives
	1 Introduction
	2 Induction Motor Faults
	3 Mechanical Faults in Electric Drives
	4 Economic Aspects of Fault Detection in Electric Drives
	5 Conventional Methods of Fault Detection
	6 Model-Based Fault Detection Methods
	7 Conclusions
	References
Introduction to Condition Monitoring of Wide Area Monitoring System
	1 Introduction
		1.1 General Overview
		1.2 Advantages of Phasor Measurement Units General Overview
		1.3 Problems in Condition Monitoring
		1.4 Literature Review
	2 Methodology for Condition Monitoring
		2.1 Synchronized Measurement Technology
		2.2 Definition of a Phasor
		2.3 Phasor Measurement Concepts
	3 Applications and Benefits of Synchronized Measurement Technology
	4 Architecture of a Condition Monitoring System
		4.1 System Description
	5 Results and Discussion
		5.1 Performance of PMU1
		5.2 Test Under Normal Operating Conditions
		5.3 Test Under Faulted State Condition
		5.4 Performance of PMU2
		5.5 Test Under Normal Operating Conditions
		5.6 Test Under Faulted State Conditions
		5.7 Performance of PMU3
		5.8 Test Under Normal Operating Conditions
		5.9 Test Under Faulted State Conditions
		5.10 Phase Deviation Normal to Fault State
	6 PMU Measurement Evaluation
		6.1 Test Under Nominal Frequency
		6.2 Placement of PMU
	7 Conclusion
	References
Introduction to Condition Monitoring of Electrical Systems
	1 Introduction
	2 What Is Condition Monitoring (in Context of Electrical Systems)?
	3 Why Condition Monitoring of Electrical Apparatus?
	4 Condition Monitoring of Power Transformer
		4.1 Oil Infrared Spectroscopy Technique
		4.2 Oil Dielectric Breakdown Test
		4.3 Du-Nouy Ring Method Test
		4.4 Dissolved Gas Analysis (DGA) Test
		4.5 SFRA Test
		4.6 Vibration-Based Monitoring
		4.7 Sound-Level Monitoring
		4.8 Infrared Temperature Monitoring
		4.9 Wireless Monitoring System
	5 Overhead Transmission Lines
		5.1 Failure Modes of an Overhead Transmission Line
		5.2 Condition Monitoring of Transmission Line
	6 Condition Monitoring of Towers
		6.1 Failure Modes of an Electrical Power Transmission Tower
		6.2 Condition Monitoring Techniques of Tower
	7 Condition Monitoring of Relay
		7.1 Problems Pertaining to Testing of Protective Relays
		7.2 Tests by Type of Protective Relay
		7.3 Periodic Inspection of Protective Relays
	8 Conclusion
	References
Condition Monitoring, and Fault Detection and Diagnostics of Wind Energy Conversion System (WECS)
	1 Introduction
	2 Methodology
	3 Brief Detail of WECS
	4 Condition Monitoring of WECS
		4.1 Rotor CM of WECS
		4.2 Gearbox CM of WECS
		4.3 Main Shaft CM of WECS
		4.4 Generator CM of WECS
		4.5 Bearing CM of WECS
		4.6 Power Electronics Converters CM of WECS
		4.7 Research Gap Finding and Suggestion
	5 Conclusion
	References
Condition Monitoring, Fault Detection and Diagnosis (FDD) of Photovoltaic System and Its Approaches
	1 Introduction
		1.1 General Overview
	2 Methodology
		2.1 PV Modeling
		2.2 Faults in PV System
		2.3 Techniques for Fault Detection
	3 Identified Research Gaps
	4 Conclusions
	References
Introduction to Condition Monitoring of PV System
	1 Introduction
		1.1 Need for Condition Monitoring in PV Systems
		1.2 The Need for AC Side Monitoring
		1.3 The Need for DC Side Monitoring
		1.4 Objectives of Performance Assessment
	2 Methods of Monitoring
	3 Monitoring System Considerations
	4 Sensor Measurements
		4.1 Inverter Level Measurements
		4.2 String/Module Level Measurement
		4.3 Measurement Resolution Considerations
	5 Conclusions
	References
Soft Computing Methods and Its Applications in Condition Monitoring of DGS—A Review
	1 Introduction
	2 Soft Computing
		2.1 Artificial Neural Networks
		2.2 Fuzzy Logic(FL)
		2.3 Optimization Techniques
	3 Summary
	References
Introduction to Conditioning Monitoring of Mechanical Systems
	1 Introduction
	2 Need of Condition Monitoring
	3 Condition Monitoring Methodology
	4 In the Condition Monitoring Techniques Various Fault Diagnosis Methods Are Involves
		4.1 Performance-Based Condition Monitoring
		4.2 Vibration Signature-Based Condition Monitoring
		4.3 Lubrication Oil Condition Monitoring
		4.4 Acoustic Emission (AE) and Noise Monitoring
		4.5 Pressure Monitoring
		4.6 Temperature Monitoring
		4.7 Wear Monitoring
	5 Condition Monitoring Methodology
		5.1 Direct Monitoring Method
		5.2 Indirect Monitoring Method
	6 Sensors for Condition Monitoring
		6.1 Force Sensor
		6.2 Temperature Sensor
		6.3 Vibration Transducers
		6.4 Sound Monitoring
	7 Analysis of Sensor Signals
		7.1 Sensor Signal Signatures Type
		7.2 Time-Domain Analysis
		7.3 Frequency-Domain Analysis
	8 Applications of Condition Monitoring in Mechanical Systems
		8.1 Cutting Tools Condition Monitoring
		8.2 Machine Tools Condition Monitoring
		8.3 IC Engine Condition Monitoring
		8.4 Power Plant Condition Monitoring
		8.5 Mechanical System Condition Monitoring
	9 Conclusion
	References
Novel Application of Artificial Neural Network Techniques for Prediction of Air Pollutants Using Stochastic Variables for Health Monitoring: A Review
	1 Introduction
	2 Artificial Neural Network for Air Quality Prediction
		2.1 Monthly Air Pollutant Prediction
		2.2 Daily Air Pollution Prediction
		2.3 Hourly Air Pollutant Prediction
	3 Identified Research Gap from Literature Survey
	4 Conclusion
	References
Application of Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems
Intelligent System for Monitoring and Recognising the Type of Domestic Harmonic Loads
	1 Introduction
	2 Background of S-Transform and Proposed Methodology
		2.1 Continuous S-Transform (CST)
		2.2 Discrete S-Transform (DST)
		2.3 Proposed Methodology
	3 Practical Setup
	4 Case Studies and Discussion
		4.1 Case 1—Central Processing Unit
		4.2 Case 2—Flickering Tube Light
		4.3 Case 3—Fluorescent Lamp
	5 Parameter Extraction
	6 Design of Fuzzy Logic Decision Box
	7 Conclusions
	References
Fault Analysis of Variable Frequency Transformer (VFT) for Power Transfer In-Between Synchronous Grids
	1 Introduction
	2 VFT Modeling
	3 Steady State Analysis of VFT
		3.1 VFT Simulation Model for Steady State Analysis
		3.2 Simulation Results of Steady State Analysis
	4 Fault Analysis of VFT
		4.1 VFT Simulation Model for Fault Analysis
		4.2 Simulation Results of Fault Analysis
	5 Conclusion
	References
Performance Enhancement and Extension of DGA-Based Transformer Fault Diagnosis Methods Using Soft-Computing Techniques
	1 Introduction
		1.1 General Overview
		1.2 Literature Review and Research Gap Findings
	2 Methodology
		2.1 Using Fuzzy Logic and ANFIS Models for DGA-Based Transformer Fault Diagnosis
		2.2 Composite DGA Method for Diagnosing Incipient Boundary and Dominant Faults [23]
		2.3 Energy Weighting of Fault Gases and Severity of Diagnosed Fault [23, 25]
		2.4 Effect of Energy Weighting of Fault Gases on DGA-Based Fault Diagnosis [26]
		2.5 Multiple Incipient Fault Detection Using DGA [27]
		2.6 Smart Diagnosis with Fault Interpretation Matrix (FIM) [24]
	3 Results and Discussions
		3.1 Performance Analysis of ANFIS Model for Identification and Location of Incipient Faults of Transformer [28]
		3.2 Comparison of Performance of Fuzzy Logic and ANFIS Models [20]
		3.3 Testing of Composite DGA Method to Resolve Boundary Issue in Duval Triangle [23, 25]
		3.4 Performance Comparison of Energy Weighted ANN DGA Model [26]
		3.5 Multiple Incipient Fault Detection Using Extended Duval Triangle Method [27]
		3.6 Testing of FIM-Based Smart DGA Data Interpretation Method [24]
	4 Conclusions
	References
Big-Data Analytics Based Energy Analysis and Monitoring for Multi-storey Hospital Buildings: Case Study
	1 Introduction
	2 Critical Components Identification Within a Building
	3 Data Set Used for Study
	4 Analysis of Energy Consumption in Hospital Building
		4.1 Case Study #1 (Hospital Type—Inpatient)
		4.2 Case Study #2 (Hospital Type—Outpatient Center)
	5 Conclusion
	References
Maximum Power Extraction and Monitoring from Wind Power Generation System Using Intelligent Controllers
	1 Introduction
		1.1 Overview
		1.2 Literature Review
	2 Proposed WECS Design
		2.1 Introduction
		2.2 Wind Energy Conversion Systems
		2.3 Control Methods
		2.4 Why DFIG Is Preferred?
		2.5 Proposed Simulation Model of WT
	3 Results and Discussions
		3.1 Comparative Analysis
	4 Conclusion
	5 Future Work
	References
Novel Applications of Soft Computing Techniques for Comparative Analysis of Maximum Power Point Tracking in Solar Photo-Voltaic System Under Perturb Conditions
	1 Introduction
	2 Depiction of Proposed SPV Framework
	3 Proposed SPV Framework Using P&O-Based MPPT Technique
		3.1 Modeling and Simulation of P&O MPPT Controller
		3.2 Simulation Results and Discussions
	4 Proposed SPV System Using FL-Based MPPT Technique
		4.1 Modeling and Analysis of FL-Based MPPT Controller for SPV System
		4.2 Simulation Results and Discussions
	5 Proposed System Using ANN-Based MPPT Controller
		5.1 Experimental Setup of SPV Emulator
		5.2 Modeling and Analysis of ANN-Based MPPT
		5.3 Simulation Results and Discussions
	6 Proposed System Using ANFIS-Based MPPT Controller
		6.1 Modeling and Analysis of ANFIS-Based MPPT
		6.2 Simulation Results and Discussions
	7 Comparative Analysis of Various MPPT Controllers for Proposed SPV System
	8 Conclusions
	References
Novel Application of Relief Algorithm in Cascade ANN Model for Prognosis of Photovoltaic Maximum Power Under Sunny Outdoor Condition of Sikkim India: A Case Study
	1 Introduction
	2 Methodology
		2.1 Experimental Setup
		2.2 Arduino and DHT Sensor
		2.3 Database of Experimental Readings
		2.4 Relief Attribute Evaluator
		2.5 Artificial Neural Network
		2.6 Cascade Artificial Neural Network (CANN)
	3 Results and Discussions
		3.1 Relevant Input Variable Selection Using Relief Attribute Evaluator
	4 Conclusions
	References
Data-Driven Intelligent Model for Sale Price Prediction and Monitoring of a Building
	1 Introduction
	2 Brief Detail of Dataset
	3 Methodology
		3.1 Proposed Approach
		3.2 Implementation of Artificial Neural Network (ANN)
	4 Results and Discussion
		4.1 Training and Testing Dataset Formation
		4.2 Sale Price Interpretation and Monitoring
	5 Conclusions
	References
Artificial Neural Networks Based Condition Monitoring of Air Pollutants for Allahabad Cities in India
	1 Introduction
	2 Methodology
		2.1 Data Collection
		2.2 Artificial Neural Network
	3 Results and Discussions
	4 Conclusion
	References
Theoretical and Experimental Investigation of Spur Gearbox with Cracked Pinion Tooth Using Dynamic Model for Condition Monitoring
	1 Introduction
		1.1 General Overview
		1.2 Literature Review and Research Gap Findings
	2 Methodology
		2.1 Dynamic Modeling of a Spur GearBox with Crack at Pinion Tooth Root
		2.2 Analytical Dynamic Response of Spur Gear System with Tooth Root Crack
		2.3 Sideband Analysis of Frequency Spectrum
		2.4 Experimental Setup and Method of Testing
	3 Results and Discussions
	4 Conclusions
	References
Experimental Study of Sideband Harmonics in Vibration Spectrum of Two-Stage Planetary Gear Box for Condition Monitoring
	1 Introduction
	2 Experimental Setup
	3 Test Procedure and Results
	4 Results and Discussion
	5 Conclusion
	References
Vibration-Based Fault Diagnosis of a Bevel and Spur Gearbox Using Continuous Wavelet Transform and Adaptive Neuro-Fuzzy Inference System
	1 Introduction
	2 Test Rig and Data Acquisition
	3 Implementation of IAR Technique
	4 Transformation to Time–Frequency Domain
	5 ANFIS as a Hybrid Intelligent Classifier
	6 Results and Discussions
	7 The Procedure Applied to Spur Gearbox Fault Diagnosis
	8 Conclusion
	References