Intelligent Fault Diagnosis and Prognosis for Engineering Systems
Интеллектуальная диагностика и прогнозирование отказов для инженерных систем
2006-09-13
SCID: 54.1/fpaawvzh
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condition-based maintenancefault diagnosisfault prognosisprognostics and health managementvibration-based fault detection
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Abstract (AI)
PREFACE. ACKNOWLEDGMENTS. PROLOGUE. 1 INTRODUCTION. 1.1 Historical Perspective. 1.2 Diagnostic and Prognostic System Requirements. 1.3 Designing in Fault Diagnostic and Prognostic Systems. 1.4 Diagnostic and Prognostic Functional Layers. 1.5 Preface to Book Chapters. 1.6 References. 2 SYSTEMS APPROACH TO CBM/PHM. 2.1 Introduction. 2.2 Trade Studies. 2.3 Failure Modes and Effects Criticality Analysis (FMECA). 2.4 System CBM Test-Plan Design. 2.5 Performance Assessment. 2.6 CBM/PHM Impact on Maintenance and Operations: Case Studies. 2.7 CBM/PHM in Control and Contingency Management. 2.8 References. 3 SENSORS AND SENSING STRATEGIES. 3.1 Introduction. 3.2 Sensors. 3.3 Sensor Placement. 3.4 Wireless Sensor Networks. 3.5 Smart Sensors. 3.6 References. 4 SIGNAL PROCESSING AND DATABASE MANAGEMENT SYSTEMS. 4.1 Introduction. 4.2 Signal Processing in CBM/PHM. 4.3 Signal Preprocessing. 4.4 Signal Processing. 4.5 Vibration Monitoring and Data Analysis. 4.6 Real-Time Image Feature Extraction and Defect/Fault Classification. 4.7 The Virtual Sensor. 4.8 Fusion or Integration Technologies. 4.9 Usage-Pattern Tracking. 4.10 Database Management Methods. 4.11 References. 5 FAULT DIAGNOSIS. 5.1 Introduction. 5.2 The Diagnostic Framework. 5.3 Historical Data Diagnostic Methods. 5.4 Data-Driven Fault Classification and Decision Making. 5.5 Dynamic Systems Modeling. 5.6 Physical Model-Based Methods. 5.7 Model-Based Reasoning. 5.8 Case-Based Reasoning (CBR). 5.9 Other Methods for Fault Diagnosis. 5.10 A Diagnostic Framework for Electrical/Electronic Systems. 5.11 Case Study: Vibration-Based Fault Detection and Diagnosis for Engine Bearings. 5.12 References. 6 FAULT PROGNOSIS. 6.1 Introduction. 6.2 Model-Based Prognosis Techniques. 6.3 Probability-Based Prognosis Techniques. 6.4 Data-Driven Prediction Techniques. 6.5 Case Studies. 6.6 References. 7 FAULT DIAGNOSIS AND PROGNOSIS PERFORMANCE METRICS. 7.1 Introduction. 7.2 CBM/PHM Requirements Definition. 7.3 Feature-Evaluation Metrics. 7.4 Fault Diagnosis Performance Metrics. 7.5 Prognosis Performance Metrics. 7.6 Diagnosis and Prognosis Effectiveness Metrics. 7.7 Complexity/Cost-Benefit Analysis of CBM/PHM Systems. 7.8 References. 8 LOGISTICS: SUPPORT OF THE SYSTEM IN OPERATION. 8.1 Introduction. 8.2 Product-Support Architecture, Knowledge Base, and Methods for CBM. 8.3 Product Support without CBM. 8.4 Product Support with CBM. 8.5 Maintenance Scheduling Strategies. 8.6 A Simple Example. 8.7 References. APPENDIX. INDEX.
Key Findings
1
Comprehensive framework for designing fault diagnostic and prognostic (CBM/PHM) systems, including functional layers and system requirements.
2
Coverage of fault prognosis techniques spanning model-based, probability-based, and data-driven prediction methods, accompanied by case studies.
3
Definition and discussion of performance metrics for diagnosis and prognosis, including feature-evaluation, effectiveness, and cost-benefit/complexity analyses.
4
Detailed treatment of sensor technologies and sensing strategies, including sensor placement, wireless sensor networks, and smart sensors.
5
Logistics and product-support strategies for operational CBM, including maintenance scheduling and architectures for knowledge bases and support.
6
Presentation of diverse fault diagnosis methods: historical data, data-driven classification, dynamic modeling, physical model-based, model-based reasoning, and case-based reasoning, with applied cases (e.g., engine bearing vibration).
7
Signal processing and data management methods for CBM/PHM, including preprocessing, vibration monitoring, real-time image feature extraction, virtual sensors, and data fusion.
8
Systems-level CBM/PHM approach covering trade studies, FMECA, test-plan design, performance assessment, and operational impact case studies.
Research Object
Condition-based maintenance and prognostics/health management (CBM/PHM) systems for engineering systems
Research Subject
Intelligent fault diagnosis and prognosis methods, including sensors, signal processing, data-driven and model-based diagnostic and prognostic techniques, performance metrics, and support/logistics for CBM/PHM
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2006-09-13
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