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.

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Intelligent Fault Diagnosis and Prognosis for Engineering Systems

The effects of pitting corrosion on the fatigue behavior of bare 7075-T6 aluminum alloy were investigated. Pitting corrosion decreased the fatigue lives by a factor of about 6 to 8. The fatigue lives were also calculated assuming an equivalent initial flaw corresponding to pits of average and maximum dimensions. The measured fatigue lives generally agreed with the predictions using the average rather than the maximum pit size as the initial crack size. This result could be explained by the pit size distributions offering a significantly larger population of pits near the average size. This work has demonstrated the promise of standardized spray tests for obtaining quantitative measures of corrosion that can be used as inputs in analytical models for fatigue life prediction for evaluating integrity of aircraft structures.

Effects of pitting corrosion on the fatigue behavior of aluminum alloy 7075-T6: modeling and experimental studies

Numerical investigation on stress concentration of corrosion pit

The nucleation of fatigue cracks from corrosion pits was investigated by conducting fatigue experiments on open-hole specimens of a 2024-T3 aluminum (bare) alloy in 0.5 M NaCl solution at room temperature and different load frequencies from 0.1 to 20 Hz. The maximum cyclic stresses applied at the hole ranged from 144 to 288 MPa and the load ratio, R , was 0.1. A specimen subjected to pre-corrosion in the NaCl solution prior to corrosion fatigue was also investigated. Pitting was found to be associated with constituent particles in the hole and pit growth often involved coalescence of individual particle-nucleated pits. Fatigue cracks typically nucleated from one or two of the larger pits, and the size of the pit at which the fatigue crack nucleates is a function of stress level and load frequency. The observations indicate that the nucleation of corrosion fatigue cracks essentially results from a competition between the processes of pitting and crack growth. Pitting predominates in the early stage of the corrosion fatigue process, and is replaced by corrosion fatigue crack growth. Based on these results, two criteria are proposed to describe the transition from pit growth to fatigue crack growth: (1) the stress intensity factor of the equivalent surface crack has to reach the threshold stress intensity factor, Δ K th , for fatigue crack growth, assuming that a corrosion pit may be modeled by an equivalent semi-elliptical surface crack, and (2) the time-based corrosion fatigue crack growth rate also exceeds the pit growth rate.

Transition from pitting to fatigue crack growth—modeling of corrosion fatigue crack nucleation in a 2024-T3 aluminum alloy

Novel images of the evolution of stress corrosion cracks from corrosion pits

Fatigue crack initiation behavior of low-alloy steel in 90 C deionized water was investigated. It was observed that the corrosion fatigue process composed three stages, namely, pit growth,...

Y. Kondo

Papers

Prediction of Fatigue Crack Initiation Life Based on Pit Growth