An Efficient Algorithm for Finding Minimal Overconstrained Subsystems for Model-Based Diagnosis

Structural analysis for fault diagnosis of models with constraints

“…Such sets can be used to define diagnostic tests. By analyzing the structural model, all MSO sets can be obtained and can thus be used to define a set of tests Krysander et al [2008]. One way of transferring an MSO into a test is to eliminate all unknown signals in the MSO set which results in a single equation involving known signals only, which can be used to check consistency of the submodel.…”

“…Using small subsets of model equations is a standard tool Krysander et al [2008], Blanke et al [2003], Pulido and Gonzalez [2004] to obtain equality relations. These works do not cover models with inequalities or the case with inequality redundancy relations that allows for even smaller subset of model equations to be used for deriving tests.…”

“…The method is based on the approach in Krysander et al [2008] where an efficient method is developed to find all MSO sets in a structural model. The algorithm is a topdown approach that traverses all PSO sets in the model and it is therefore straightforward to modify the MSO algorithm to find all PSO sets with a specified redundancy.…”

“…Also, fault isolation is made possible by considering different and well selected submodels when designing each test. Such submodels are called Minimal Structurally Overdetermined sets of equations (MSO) Krysander et al [2008], or Analytical Redundancy Relations (ARR) Blanke et al [2003], or Minimal Evaluation Chain (MEC) Pulido and Gonzalez [2004]. Here, the term MSO will be used.…”

A Method for Sensor Placement Taking into Account Diagnosability Criteria

“…The performance of a diagnostic system is highly dependent on the set K and, consequently, dependent on the set K Σ , which highly depends on the dataflows, i.e., on the observations. Additional sensors lead to addtional constraints in K Σ and, therefore, to new sets in K. K can be obtained from combinations of constraints from K Σ using possible conflict generation (Pulido and Alonso, 2002), a bipartite graph (Blanke et al, 2006), the Dulmage-Mendelsohn decomposition (Krysander, Aslund and Nyberg, 2008) or elimination rules (Ploix, Désinde and Touaf, 2005). Basically, once K has been generated, it is possible to compute the performance of the diagnostic system in terms of detectability, discriminability or discernability, and diagnosability.…”