A Combined Data-Driven and Model-Based Residual Selection Algorithm for Fault Detection and Isolation
Selecting residual generators for detecting and isolating faults in a system is an important
step when designing model-based diagnosis systems. However, finding a suitable
set of residual generators to fulfill performance requirements is complicated by model
uncertainties and measurement noise which have negative impact on fault detection performance.
The main contribution is an algorithm for residual selection which combines model-based
and data-driven methods to find a set of residual generators that maximizes
fault detection and isolation performance. Based on the solution from the residual selection
algorithm, a generalized diagnosis system design is proposed where test quantities are
designed using multi-variate residual information to improve detection performance.
To illustrate the usefulness of the proposed residual selection algorithm, it is applied to
find a set of residual generators to monitor the air path through an internal combustion engine.
Daniel Jung and Christofer Sundstrom
IEEE Transactions on Control Systems Technology,
2017
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