On the Application of Artificial Intelligence Techniques to the Quality Improvement of Industrial Processes P. Georgilakis N. Hatziargyriou Schneider ElectricNational Technical University of AthensGreece

Contents 1.Quality improvement with Decision Trees (DT) 2.Quality improvement with Hybrid DT-NN Classifiers 3.Industrial applications 4.Discussion of results 5.Conclusions

Quality improvement with Decision Trees The DT is built on the basis of a learning set (LS). The LS comprises a number of preclassified (e.g., acceptable or non-acceptable) measurement sets (MS) defined by a list of candidate attributes. A suitable test is sought to divide a node, by applying the optimal splitting rule.

The optimal splitting rule decides what is the best attribute and its threshold value, so that the additional information gained through that test is maximized. The best attribute and its threshold value are obtained by sequential testing of all attributes and candidate thresholds and comparing their information gain. In the case that no test can be found with a statistically significant information gain, the node is declared a DEADEND and is not further split. Quality improvement with Decision Trees

In order to detect if a node is terminal, or sufficiently “class-pure” (i.e. the majority of its MS belong to one of the two classes), the stop splitting rule is used. More specifically: If the classification entropy of the node is lower than a minimum pre-set value H min, then the node is declared LEAF and is not further split. If not  the optimal splitting rule is applied. Quality improvement with Decision Trees

Quality Rule 1 at node 4 if (ATTR9  ) and (ATTR1  )   Acceptable quality. 5 rules in total (equal to the number of terminal nodes). Avoid leading to node 5 (acceptability index of 34.55%, or only 19 out of 55 measurement sets are acceptable. Quality improvement with Decision Trees

From DT to Entropy Network AL1  4 AL2  5 AL3  7 AL4  8 AL4  9 TL1  1 TL2  2 TL3  3 TL4  6 

Entropy Network (EN) versus the fully connected Multilayer Perceptron (MLP) The tedious task of network structure optimization (i.e. the finding of the number of hidden layers, and the optimum number of neurons per layer) is avoided. The training time is significantly reduced (more than a factor of 10), since the EN has fewer connections, and as a result fewer unknown parameters. If the EN is retrained its classification performance can be improved and to be very close to the performance of the MLP.

The ORing Layer of the EN is replaced by a single output neuron, fully connected to all neurons of the ANDing Layer and the resulted network is called Hybrid DT-NN. Next, the Hybrid DT-NN is trained again. Next, the trained Hybrid DT-NN is used to predict the quality of the test set, and, finally, to classify them accordingly, providing the Hybrid DT-NN Classifier. Quality improvement with Hybrid DT-NN Classifiers

Industrial Application - Design of Wound Cores

Transformer Iron Losses Influenced by both qualitative and quantitative parameters. There is no simple relationship among the parameters involved in the production process that expresses analytically the transformer iron losses. Importance of iron loss prediction Protects the manufacturer from paying loss penalties. Contributes in economic manufacturing.

Artificial Intelligence Has the ability to automatically learn relationships between inputs and outputs Has been applied to the quality improvement of individual core and transformer The quality improvement is achieved through better classification of iron losses (of core / transformer) prior to the manufacturing (of core / transformer)

 Quality Improvement of Individual Cores First Industrial Application :

Application on Individual Core - Attributes

Application on Individual Core - DT Avoid node 6 One way is to increase ATTR7  Inrease the actual weight of core by adding more magnetic material so that the ratio of actual over theoretical core weight ratio to become greater than 0.98 Lead the production to node 4 or 6

Application on Individual Core - Entropy Network

Application on Individual Core - Entropy Network and Hybrid DT-NN Classifier MethodStructureError (%) DT-6.0 EN HDTNNC

 Quality Improvement of Transformer Second Industrial Application :

Application on Transformer - Attributes

Application on Transformer - DT Avoid node 5 One way is to reduce ATTR1  Reduce the actual total iron losses of individual cores by removing from the transformer cores set one or more cores with high iron losses and add cores with lower losses Lead the production to node 7, 8, or 4

Application on Transformer - Entropy Network

MethodStructureError (%) DT-4.0 EN HDTNNC MLP (9 attributes) MLP (3 DT attributes) Application on Transformer - Entropy Network and Hybrid DT-NN Classifier

 Minimisation of Transformer Iron Losses Third Industrial Application :

Current grouping process Pre-measure and assign a grade (quality category) to each individual core and then combine higher and lower graded individual cores to achieve an “average” value for the entire transformer. Objective of grouping process To reduce the variation in iron losses of assembled transformers.

GA Representation

Two-point crossover operator

1.Input transformer design data and measurements on L small cores and L large cores. 2.Assign integer numbers 1 through L to the small cores, and L+1 through 2*L to the large cores. 3.Initialize the population, set the probabilities of crossover and mutation, and define the GA termination criterion. 4.Estimate the fitness (i.e. the total predicted, by the MLP, iron losses). Optimal Solution Using GAs (1)

Optimal Solution Using GAs (2) 5.Parent selection, crossover, mutation, and creation of the next generation. 6.Estimate the fitness. 7.If the termination criterion is satisfied, then proceed to step 8, else go to step 5. 8.Output the optimal arrangement of cores for all the L/2 transformers (optimal solution), the predicted iron losses of each of the L/2 transformers, and the total predicted iron losses. 9.Assemble the L/2 transformers using the optimal arrangement of cores.

Results (1) Grouping 100 small and 100 large cores of the same production batch of 50 transformers, 160 kVA, 50 Hz. Minimum total iron losses of W, at generation 85.

Results (2) The evaluation of the genetic algorithm is based on the AARE. For the specific example, the AARE is 0.57%. In general, the AARE is reduced by 50% (3.15%  1.6%).

Advantages for the transformer customer Significant contribution in the economic operation of the electric network, due to: Iron loss reduction, and The fact that iron losses exist 24 hours per day, 365 days per year, even if the transformer is operating without load. Customer satisfaction, due to lower losses, and higher economy.

Advantages for the transformer manufacturer Reduction in iron loss variation, leading to the increase of the reliability of the manufacturer. Reduction of the cost of materials: Magnetic materials: 2.5%  (2001: 2.0 Millions USD) Copper: 1%  (2001: 1.7 Millions USD) Insulating materials: 1%  (2001: 0.4 Millions USD) Oil: 2%  (2001: 0.4 Millions USD) Avoid paying loss penalties. Increase of market share.

Conclusions The application of the DT method provides rules useful for the quality improvement of the industrial processes A hybrid DT-NN classifier is proposed for the quality improvement of industrial processes The method was applied to the quality improvement of indivudual core and transformer and was compared with other methods A new GA based grouping process was proposed, in order to reduce iron losses