INTRODUCTION OF INTELLIGENT MACHINE FAULT DIAGNOSISAND PROGNOSIS -- CONTENTS -- PREFACE -- ACKNOWLEDGMENTS -- ABOUT THE AUTHORS -- INTRODUCTION -- 1.1. BACKGROUND -- 1.2. MAINTENANCE STRATEGY -- 1.3. DEFINITION OF FAULT DIAGNOSIS AND PROGNOSIS -- 1.4. CONDITION BASED MAINTENANCE SYSTEM ARCHITECTURE -- 1.5. FAULT DIAGNOSIS FRAMEWORK -- 1.6. ROLE OF CONDITION MONITORING, FAULT DIAGNOSIS AND PROGNOSIS -- 1.7. PROBLEMS DURING IMPLEMENTATION -- 1.8. RELATED TECHNIQUES -- 1.9. REFERENCES -- DATA ACQUISITION, PROCESSING AND ANALYSIS -- 2.1. INTRODUCTION -- 2.2. DATA ACQUISITION -- 2.2.1. Selecting A Proper Measure -- 2.2.2. Vibration Transducers -- 1) Proximity Probes -- 2) Velocity Transducers -- 3) Accelerometers -- 4) Key-Phasor -- 2.2.3. Transducer Selection -- 2.2.4. Transducer Mounting -- 1) Stud Mounting -- 2) Cement Mounting -- 3) Wax Mounting -- 4) Adhesive Mounting -- 5) Magnetic Mounting -- 6) Hand-Held -- 2.2.5. Transducer Location (Measuring Point) -- 2.2.6. Frequency Spans -- 2.2.7. Data Display -- 1) Time Waveform -- 2) Spectrum -- 3) Orbit -- 2.3. DATA PROCESSING -- 2.3.1. Data preprocessing -- 1) Wavelet Transform -- Continuous Wavelet Transform (CWT) -- 2) Averaging -- 3) Enveloping -- 4) Cesptrum -- 2.4. DATA ANALYSIS -- 2.4.1. Features in Time Domain -- 1) Cumulants -- 2) Upper and Lower Bound of Histogram -- 4) Auto-Regression Coefficients -- 2.4.2. Features in Frequency Domain -- 1) Fourier Transform -- 2) Spectral Analysis -- 3) Frequency parameter indices -- 4) Higher order spectra (HOS) -- 2.4.3. Features in Time-Frequency Domain -- 1) Short Time Fourier Transform (STFT) -- 2) Wavelet Transform -- 2.5. REFERENCES -- FEATURE EXTRACTION AND CLUSTERING -- 3.1. INTRODUCTION -- 3.2. DEFINITION OF SOME BASIC CONCEPTS -- 3.2.1. Pattern and Feature Vector -- 3.2.2. Class -- 3.3. PARAMETER EVALUATION TECHNIQUE.

3.4. PRINCIPAL COMPONENT ANALYSIS (PCA) -- 3.5. INDEPENDENT COMPONENT ANALYSIS (ICA) -- 3.6. KERNEL PCA -- 3.7. KERNEL ICA -- 3.8. FISHER DISCRIMINANT ANALYSIS (FDA) -- 3.9. LINEAR DISCRIMINANT ANALYSIS (LDA) -- 3.10. GENERALIZED DISCRIMINANT ANALYSIS (GDA) -- 3.11. CLUSTERING -- 3.11.2 K-Means Clustering -- 3.11.3. Hierarchical Clustering -- 3.12. OTHER TECHNIQUES -- 3.13. REFERENCES -- FEATURE SELECTION -- 4.1. INTRODUCTION -- 4.2. INDIVIDUAL FEATURE EVALUATION (IFE) BASED ON SPACE DISTRIBUTION -- 4.3. CONDITIONAL ENTROPY -- 4.4. BACKWARD FEATURE SELECTION -- 4.5. FORWARD FEATURE SELECTION -- 4.6. BRANCH AND BOUND FEATURE SELECTION -- 4.7. PLUS L-TAKE AWAY R FEATURE SELECTION -- 4.8. FLOATING FORWARD FEATURE SELECTION -- 4.9. DISTANCE EVALUATION TECHNIQUE -- 4.10. TAGUCHI METHOD-BASED FEATURE SELECTION -- 4.11. GENETIC ALGORITHM -- 4.11.1. General concept -- 1) Selection -- 2) Crossover -- 3) Mutation -- 4.11.2. Differences from other Traditional Methods -- 4.11.3. Simple Genetic Algorithm (SGA) -- 4.11.4 Feature Selection Using GA -- 4.12. SUMMARY -- 4.13. REFERENCES -- FAULT CLASSIFICATION ALGORITHMS -- 5.1. INTRODUCTION -- 5.2. LINEAR CLASSIFIER -- 5.2.1. Linear Separation of Finite Set of Vectors -- 5.2.2. Perceptron Algorithm -- 5.2.3 Kozinec's Algorithm -- 5.2.4. Multi-Class Linear Classifier -- 5.3. QUADRATIC CLASSIFIER -- 5.4. BAYESIAN CLASSIFIER -- 5.5. K-NEAREST NEIGHBORS (K-NN) -- 5.6. SELF-ORGANIZING FEATURE MAP (SOFM) NEURAL NETWORK -- 5.7. LEARNING VECTOR QUANTIZATION (LVQ) NEURAL NETWORK -- 5.8. RADIAL BASIS FUNCTION (RBF) NEURAL NETWORK -- 5.9. ART-KOHONEN NEURAL NETWORK (ART-KNN) -- 5.10. SUPPORT VECTOR MACHINES (SVMS) -- 5.10.1. Wavelet SVM -- 5.10.2. Multi-Class Classification -- 5.10.3. Sequential Minimal Optimization (SMO) -- 5.11. DECISION TREE -- 5.11.1. Building Decision Tree -- 5.11.2 Pruning Decision Tree.

5.12. RANDOM FOREST -- 5.12.1. Random Forest -- 1) CART Methodology -- 2) Tree Building -- 3) Stopping Tree Building -- 5.12.2. Random Forests Algorithm (RF) -- 1) Two Randomized Procedures in RF Tree Building -- 2) Convergence of RF -- 3) Accuracy of RF Depending on Strength and Correlation -- 5.12.3. Genetic Algorithm -- 5.13. ADAPTIVE NEURO-FUZZY INTEGRATED SYSTEM (ANFIS) -- 5.13.1 Classification and Regression Trees (CART) -- 1) Tree Building -- 2) Tree Pruning -- 5.13.2. Adaptive Neuro-Fuzzy Inference System (ANFIS) -- 1) Architecture of ANFIS Based on CART -- 2) Learning Algorithm of ANFIS -- 5.14. CASE STUDIES: FAULT DIAGNOSIS OF INDUCTION MOTORS -- 5.14.1. Wavelet SVM -- 1) Experiment And Data Acquisition -- 2) Feature Calculation -- 3) Feature Extraction and Reduction -- 4) Training and Classification -- 5) Result And Discussion -- 5.14.2. Decision Tree -- 5.14.3 Random Forest -- 1) Data Acquisition and Feature Calculation -- 2) Fault Diagnosis Result and Discussion -- 5.14.4. Cart-Anfis -- 1) Data Acquisition -- 2) Feature Calculation -- 3)Feature Selection and Classification -- 5.15. REFERENCES -- DECISION FUSION ALGORITHMS -- 6.1. INTRODUCTION -- 6.2. FUSION APPLICATION AREAS -- 6.3. FUSION ARCHITECTURES -- 6.3.1. Data-Level Fusion -- 6.3.2. Feature-Level Fusion -- 6.3.3. Decision-Level Fusion -- 6.4. FUSION TECHNIQUES AT DECISION-LEVEL -- 6.4.1. Voting Method -- 6.4.2. Bayesian Belief Fusion -- 6.4.3. Behavior Knowledge Space (BKS) -- 6.4.4. Dempster-Shafer Theory -- 6.4.5. Multi-Agent Fusion -- 6.4.6. Decision Templates (DTS) -- 6.5. CLASSIFIER SELECTION -- 6.5.1 2-Classifier Correlation Analysis -- 6.5.2. Multi-Classifier Correlation Analysis -- 6.6. DECISION FUSION SYSTEM -- 6.6.1. Level 1 To Level 3: Preparation for Decision Fusion -- 6.6.2. Level 4: Data Fusion at Decision Level -- 6.6.3. Level 5: Classifier Selection.

6.6.4. Level 6: Decision Fusion -- 6.7 FAULTS DIAGNOSIS OF TEST-RIG MOTORS USING FUSION TECHNIQUES -- 6.7.1. Data Acquisition -- 6.7.2. Feature Calculation and Classification -- 6.7.3. Classifiers Selection and Fusion -- 6.7.4.Classifiers Fusion Comparison -- 6.8. FAULTS DIAGNOSIS OF ELEVATOR MOTOR USING FUSION TECHNIQUES -- 6.8.1. Data Acquisition -- 6.8.2. Feature Calculation and Classification -- 6.8.3. Classifiers Fusion -- 6.9. DECISION-LEVEL FUSION DIAGNOSIS USING TRANSIENT CURRENT SIGNAL -- 6.9.1. Experiments and Data Acquisition -- 6.9.2. Signal Preprocessing and Wavelet Transform -- 6.9.3. Features Calculation and Classification -- -- 6.9.4. Fusion Performance Evaluation -- 6.10. REFERENCES -- FAULT PROGNOSIS ALGORITHMS -- 7.1. INTRODUCTION -- 7.2. PROGNOSIS APPROACHES -- 7.2.1. Rule-Based Approaches -- 7.2.2. Fuzzy Logic Approaches -- 7.2.3. Model-Based Approaches -- 1) Physics-Based Model -- 2) System Dynamic Model -- 3) Probabilistic Model -- 7.2.4. Trend-Based Evolutionary Approach -- 7.2.5. Data-Driven Model Based Approach -- 1) Neural Networks -- 2) Support Vector Regression (SVR) -- (1) Linear Support Vector Regression -- 2) Nonlinear Support Vector Regression -- 7.2.6. State Estimator-Based Prognosis -- 7.2.7. Statistical Reliability and Usage-Based Approaches -- 7.2.8. Adaptive Prognosis -- 7.2.9. Data Miningand Automated Rule Extraction -- 7.2.10. Distributed Prognosis System Architecture -- 7.3. APPLICATIONS -- 7.3.1. Bearing Prognosis -- 1) Spall Initiation Model -- 2) Spall Progression Model -- 7.3.2. Gear Prognosis -- 7.3.3. Low-Methane Compressor Prognosis -- 7.3.4. Machine-Tool Prognosis -- 7.4. REFERENCES -- APPENDIX -- INDEX.