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Pattern Recognition Approach for Fault Diagnosis of DAMADICS Benchmark

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Title: Pattern Recognition Approach for Fault Diagnosis of DAMADICS Benchmark


1
Pattern Recognition Approach for Fault Diagnosis
of DAMADICS Benchmark
  • Cosmin Bocaniala
  • University Dunarea de Jos from Galati, Romania
  • Andrzej Marciniak
  • University of Zielona Gora, Poland
  • Jose Sa da Costa
  • Instituto Superior Tecnico, Lisbon, Portugal

2
...
  • Andrzej Marciniak contribution

3
A Novel Fuzzy Classifier
  • fault diagnosis may be seen as a classification
    problem
  • building a map between symptoms space and the set
    of faulty states
  • two main advantages of the developed fuzzy
    classifier
  • the high accuracy with which it distinguishes
    between different categories
  • the fine precision of discrimination inside
    overlapping zones

4
Previous work
  • three main directions of using fuzzy classifiers
  • neuro-fuzzy systems
  • robust to uncertainties and noise
  • collections of fuzzy rules
  • transparent symptoms-faults relationships via
    linguistic terms
  • represent normal state and each faulty state as
    fuzzy subsets of the symptoms space

5
Point-to-point similarity
  • the similarity s(u,v) between two points is
    computed using a dissimilarity measure d(u,v)
  • the ß parameter plays the role of a threshold
    value for the similarity measure
  • single or hybrid similarity measures

6
Point-to-set similarity
  • the similarity measure between two points can be
    extended to a similarity measure between a data
    point and a set

7
Induced fuzzy sets
  • the fuzzy membership functions are induced by the
    point-to-set affinity
  • each category has associated a different ß
    parameter

8
Computational aspects
  • the main computational issue is the search for
    the set of parameters that provide the best
    performance
  • genetic algorithms (slow)
  • hill climbing (fast)
  • particle swarm optimization (best!)

9
GA vs HC
No. exp Initial Final No.calls (Method) fitness fi
tness classifier
1 (GA) 138.83 147.98 340 2 (GA) 140.40 149.54 340
3 (GA) 144.97 151.26 340 4 (GA) 140.83 149.82 340
5 (GA) 139.98 151.08 340 1 (HC) 138.83 149.54 136
2 (HC) 140.97 150.59 141 3 (HC) 142.98 151.50 108
4 (HC) 141.90 151.30 124 5 (HC) 137.90 149.78 106
10
GA vs PSO
No. exp Initial Final No.calls (Method) fitness fi
tness classifier
1 (GA) 138.83 147.98 340 2 (GA) 140.40 149.54 340
3 (GA) 144.97 151.26 340 4 (GA) 140.83 149.82 340
5 (GA) 139.98 151.08 340 1 (PSO) 124.18 151.25 100
2 (PSO) 121.62 150.03 220 3 (PSO) 117.65 146.46 1
60 4 (PSO) 127.47 151.38 140 5 (PSO) 134.07 156.30
140
11
Results on DAMADICS benchmark Step I
  • the effects of six out of the 19 faults on this
    set of sensor measurements are not
    distinguishable from the normal behavior, F4,
    F5, F8, F9, F12, F14
  • also, there can be distinguished three groups of
    faults, F3, F6, F7, F10, and F11, F15, F16,
    that share similar effects on the measurements
    and, therefore, can be easily confound with
    faults in the same group.

12
Results on DAMADICS benchmark Step I
The large overlapping between F3 and F6
13
Results on DAMADICS benchmark Step I
The large overlapping between F7 and F10
14
Results on DAMADICS benchmark Step I
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
F1 N N N N N N N N N F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F7
F2 N N N N F2 F2 F2 F2 F2 F2 F2 F19 F2 F2 F2 F2 F2 F2 F2 F2
F3 N N N F6 N N F6 F3 F3 F3 F1 F3 F6 F3 F3 F3 F3 F3 F3 F3
F6 N N N N N N N N N F6 N F6 F6 F6 F6 F6 F6 F6 F6 F3
F7 F7 F7 F7 F7 F7 F7 F7 F7 F7 F7 F7 F7 F7 F7 F7 F7 F7 F7 F7 F1
F10 N N N N N F15 F10 F1 F1 F10 F10 F10 F10 F10 F1 F7 F7 F7 F7 F7
F11 N N N N N N F11 N F11 N F11 F11 F11 F11 F11 F11 F11 F11 F11 F11
F13 N F3 F3 F3 F3 F3 F13 F13 F13 F13 F13 F13 F13 F13 F13 F13 F13 F13 F13 F13
F15 N F15 N F15 N N N F15 F15 F15 N F15 F15 F15 F15 N N F15 N F15
F16 N N N N N N N N N N N N N N F16 N F16 F15 F16 F16
F17 N N N N F17 F17 F17 F17 F17 F17 F17 F17 F17 F17 F17 F17 F17 F17 F17 F17
F18 F3 F3 F3 F3 N F18 F18 F18 F18 F18 F18 F18 F18 F18 F18 F18 F18 F18 F18 F18
F19 N N F2 F2 F2 F2 F2 F19 F19 F19 F19 F19 F19 F19 F19 F19 F19 F19 F19 F19
15
Conclusions
  • advantages high accuracy discrimination between
    different categories, and fine precision inside
    overlapping zones
  • fast parameters tuning using PSO
  • good performances on the DAMADICS benchmark, Step
    I
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