Identifying PCB Contaminated Transformers Through Active Learning

Exposure to polychlorinated biphenyals (PCBs) is hazardous to human health. The United Nations Environment Programme has decreed that nations, including Canada and USA, must eliminate PCB contaminated equipment such as transformers by 2025. To determine the PCB status of a transformer with absolute certainty, the oil mixture of the transformer must be sampled because transformers labeled as non-PCB could be cross-contaminated. Since sealed oil mixture sampling is costly, for the first time, we apply an iterative machine learning technique called active learning to classify PCB contaminated transformers while minimizing a cost metric that integrates the classification error cost and the sampling cost. We propose a dynamic sampling method to address two key issues in active learning: the sampling size per iteration and the stopping criterion of the sampling process. The proposed algorithm is evaluated using the real-world datasets from BC Hydro in Canada.