A comprehensive guide to the application and processing of
condition-based data to produce prognostic estimates of functional
health and life.
Prognostics and Health Management 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.
Written by noted experts in the field, Prognostics and Health
Management 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:
- Integrates data collecting, mathematical modelling and reliability
prediction in one volume
- Contains numerical examples and problems with solutions that help with
an understanding of the algorithmic elements and processes
- Presents information from a panel of experts on the topic
- Follows prognostics based on statistical modelling, reliability
modelling and usage modelling methods
Written for system engineers working in critical process industries and
automotive and aerospace designers, Prognostics and Health Management
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.
