Towards Distributed Diagnosis of Complex Physical Systems

1
Towards Distributed Diagnosis of Complex Physical
Systems

• Presented by-
• Gautam Biswas
• Jyoti Gandhe
• Xenofon D. Koutsoukos
• Sherif Abdelwahed

2
Motivation for Distributed Diagnosis

• Large Scale , Complex Systems deployed in
  mission-critical and safety-critical applications
  should be reliable, dependable, available, and
  operationally robust.
• Online Model based fault diagnosis with composed
  model of overall complex system makes diagnosis
  task computationally difficult
• Hard to analyze complex nonlinearities online.
  Develop qualitative reasoning techniques to make
  diagnostic analysis computationally simpler and
  robust
• Develop Distributed Diagnosis Algorithm so a
  large computationally expensive diagnosis task is
  decomposed into a set of smaller tasks that can
  be performed independently, thus reducing the
  overall complexity of online diagnosis.

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Design of the distributed algorithm

• We start with a set of possible faults in the
  system and a set of available measurements.
• Our Goal - Distributed and Complete Diagnosis
• (i.e., all faults of interest should be uniquely
  identified)
• Our Method-
• Partition the set of faults into subsets such
  that we can construct non-interacting diagnosers
  for each subset.
• Two diagnosers do not interact if they dont
  share information in establishing unique,
  globally valid diagnosis results.
• This will be true if fault subsets corresponding
  to the two diagnosers are independent i.e. they
  do not require the same set of measurements to
  achieve complete diagnosability.

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Design of the algorithm

• Establish measurements that uniquely distinguish
  a fault
• Group faults to obtain maximum number of
  independent fault subsets NP-Complete
• Heuristics of approximating algorithm
• Measurements with Discontinuities have the most
  discriminatory power for a fault
• Add next fault to the best partition for it
• Best Partition is the one whose measurement set
  most overlaps with measurements that uniquely
  identify it and causes the least number of
  combinations with other partitions.
• Complexity of the partition procedure - O (f
  m4) f, m Number of faults and measurements
  respectively