MAPREDUCE FRAMEWORK FOR ANOMALY DETECTION IN MANUFACTURING DATA MISS SIKANA TANUPABRUNGSUN A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ENGINEERING (COMPUTER ENGINEERING) FACULTY OF ENGINEERING KING MONGKUT’S UNIVERSITY OF TECHNOLOGY THONBURI

Manufacturing data is an important source of knowledge that can be used to enhance the production capability. Detecting of the causes of defects possibly leads to an improvement in the production. However, the production records generally contain an enormous set of features and the number of alert messages is redundant. Thus, it is almost impossible in practice to monitor all features at once. This research proposes the feature reduction framework, which is designed to identify a subset of informative features and the correlation groups. By monitoring fewer features, the number of alert messages can be decreased. In our methodology, manufacturing data are pre-processed and adopted as inputs. Subsequently, the feature selection process is performed by wrapping Genetic Algorithm (GA) with the k-Nearest Neighbor (kNN) classifier. To improve the performance, the proposed technique was parallelized with MapReduce. The results showed that the number of features can be reduced by 49.02% with 83.95% accuracy. In addition, with MapReduce on the cloud, the performance was increased by 17.06 times. The framework result was validated by both statistical method and expert analysts from the manufacturing industry.