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Browsing by Author "Bagchi, Saurabh"
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Item Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets(MDPI, 2023-01-02) Abdallah, Mustafa; Joung, Byung-Gun; Lee, Wo Jae; Mousoulis, Charilaos; Raghunathan, Nithin; Shakouri, Ali; Sutherland, John W.; Bagchi, Saurabh; Computer and Information Science, School of ScienceSmart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance.Item AutoForecast: Automatic Time-Series Forecasting Model S(National Science Foundation, 2022) Abdallah, Mustafa; Rossi, Ryan; Mahadik, Kanak; Kim, Sungchul; Zhao, Handong; Bagchi, Saurabh; Engineering Technology, School of Engineering and TechnologyIn this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one. In particular, we develop a forecasting meta-learning approach called AutoForecast that allows for the quick inference of the best time-series forecasting model for an unseen dataset. Our approach learns both forecasting models performances over time horizon of same dataset and task similarity across different datasets. The experiments demonstrate the effectiveness of the approach over state-of-the-art (SOTA) single and ensemble methods and several SOTA meta-learners (adapted to our problem) in terms of selecting better forecasting models (i.e., 2X gain) for unseen tasks for univariate and multivariate testbeds.