<p><b>A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life.</b><i> </i></p> <p><i>Prognostics and Health Management</i> provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improving system reliability using conditioned-based data (CBD) to the monitoring and management of health of systems. This proven approach uses electronic signatures extracted from conditioned-based electrical signals, including those representing physical components, and employs processing methods that include data fusion and transformation, domain transformation, and normalization, canonicalization and signal-level translation to support the determination of predictive diagnostics and prognostics. </p> <p>Written by noted experts in the field, <i>Prognostics and Health Management</i> clearly describes how to extract signatures from conditioned-based data using conditioning methods such as data fusion and transformation, domain transformation, data type transformation and indirect and differential comparison. This important resource:</p> <ul> <li>Integrates data collecting, mathematical modelling and reliability prediction in one volume</li> <li>Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes</li> <li>Presents information from a panel of experts on the topic</li> <li>Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods</li> </ul> <p>Written for system engineers working in critical process industries and automotive and aerospace designers, <i>Prognostics and Health Management </i>offers a guide to the application of condition-based data to produce signatures for input to predictive algorithms to produce prognostic estimates of functional health and life.</p>
