In an era of intense competition where plant operating
efficiencies must be maximized, downtime due to machinery failure
has become more costly. To cut operating costs and increase
revenues, industries have an urgent need to predict fault
progression and remaining lifespan of industrial machines,
processes, and systems. An engineer who mounts an acoustic sensor
onto a spindle motor wants to know when the ball bearings will wear
out without having to halt the ongoing milling processes. A
scientist working on sensor networks wants to know which sensors
are redundant and can be pruned off to save operational and
computational overheads. These scenarios illustrate a need for new
and unified perspectives in system analysis and design for
engineering applications.
Intelligent Diagnosis and Prognosis of Industrial Networked
Systems proposes linear mathematical tool sets that can be applied
to realistic engineering systems. The book offers an overview of
the fundamentals of vectors, matrices, and linear systems theory
required for intelligent diagnosis and prognosis of industrial
networked systems. Building on this theory, it then develops
automated mathematical machineries and formal decision software
tools for real-world applications.
The book includes portable tool sets for many industrial
applications, including:
- Forecasting machine tool wear in industrial cutting
machines
- Reduction of sensors and features for industrial fault
detection and isolation (FDI)
- Identification of critical resonant modes in mechatronic
systems for system design of R&D
- Probabilistic small-signal stability in large-scale
interconnected power systems
- Discrete event command and control for military
applications
The book also proposes future directions for intelligent
diagnosis and prognosis in energy-efficient manufacturing, life
cycle assessment, and systems of systems architecture. Written in a
concise and accessible style, it presents tools that are
mathematically rigorous but not involved. Bridging academia,
research, and industry, this reference supplies the know-how for
engineers and managers making decisions about equipment
maintenance, as well as researchers and students in the field.
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