Prognostics and Health Management: A Practical Approach to Improving System Reliability Using Conditioned-Based Data
The book provides readers with an understanding of the rationale and methodologies of a practical approach for improving system reliability using conditioned-based data (CBD) to monitor and manage the health of systems. The approach uses electronic signatures extracted from conditioned-based electrical signals, including those representing physical components, by using processing methods to include data fusion and transformation, domain transformation, and normalization, canonicalization, and signal-level translation to support the determination of predictive diagnostics and prognostics. Condition-based electrical signals, such as voltage, current, or energy, at a measured node for a device, component, or assembly (a prognostic object) exhibit characteristic changes as the prognostic object progresses from a state of no detectable damage (not impaired) to detectable damage (impaired but still functional). Eventually, as damage increases, the prognostic object functionally fails: even though it has not physically failed, the prognostic object is operationally out of specifications. The characteristic curves for given failure modes are degradation progression signatures (DPSs) and fault-to-failure progression (FFP) signatures. This book describes how to extract those signatures from CBD using conditioning methods such as data fusion and transformation, domain transformation, data type transformation, and indirect and differential comparison.