Optional SVM for Fault Diagnosis of Blast Furnace with Imbalanced Data

Fault diagnosis for blast furnace is actually a multi-class classification problem because the blast furnace may appear usually many kinds of abnormal states. Moreover, those abnormal states should be monitored and diagnosed timely and what can help workers take effective measures. Support vector machine (SVM) is state-of-the-art for many classification problems currently. But many classification tasks involve imbalanced training examples in practice. Imbalanced dataset learning is an important practical issue in machine learning, especially in support vector machine (SVM). Fault diagnosis for blast furnace is such an imbalanced data problem. A novel algorithm named optional support vector machine is proposed to solve this imbalanced data classification by pruning training sets and adding the unlabeled data and applying edited nearest neighbor (ENN) rules. Firstly, training sets of majority class are pruned in order to reduce the training time. Secondly, the algorithm selects some useful unlabelled training data and adds them to the training sets. Those samples are used to replenish the lack of training samples so that the training sets are representative. However, they may contain some noisy examples. Finally, the edited nearest neighbor rule is removed the noisy examples. The algorithm adds the unlabelled (testing) samples to balance the number of samples between the minority class and the majority one. The real-time producing data of blast furnace are used to running experiment. In order to more accurately diagnose which kinds fault happened, a binary tree multi-class classification method is adopted based on blast furnace characteristics. Simulation results show that the proposed algorithm is feasible and effective.