Mathematical Problems in Engineering / 2013 / Article / Alg 1

Research Article

Simultaneous-Fault Diagnosis of Automotive Engine Ignition Systems Using Prior Domain Knowledge and Relevance Vector Machine

Algorithm 1

Algorithm of the proposed framework for simultaneous-fault diagnosis of time-dependent ignition patterns.
Given a training dataset TRAIN_F of single-fault patterns only, a validation dataset VALID_F and a test dataset TEST_F of single-
fault and simultaneous-fault patterns (all datasets have been preprocessed by the combination of DK and WPT and PCA, as
presented in Figure 7(a)):
 (i) Train the probabilistic classifier
   includes pairwise classifiers as shown in Figure 6(b).
 (ii) f     VALID_F, prepare the probability vector
  Calculate (f) = = (Figure 7(c)).
 (iii) For k = 1 to Run a direct search technique, such as GA or PSO, M times
  Produce an initial population for the decision threshold
   (a) , find the classification vector y(f) = y = = according to (1).
   (b) Calculate the -measure with y(f) and l(f) using (12), that is, find over VALID_F,
    where l(f) = [ ] is the true classification vector for input f provided from VALID_F.
   (c) Produce next generation of
   Until convergence or matching stopping criteria, return the best solution ε as the .
 (iv) Among all , k = 1 to M, choose the one producing the highest F-measure as the optimal decision threshold .
 (v) Return the trained probabilistic classifier and the optimized decision threshold as the main components of the
  intelligent diagnostic system.
 (vi) The performance of and can be evaluated with TEST_F and as illustrated in Figure 7(d).