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For the pulping process in a pulp & paper plant that uses woodchips as raw material, the moisture content (MC) of the woodchips is a major process disturbance that affects product quality and consumption of energy, water, and chemicals. Existing woodchip MC sensing technologies have not been widely adopted by the industry due to unreliable performance and/or high maintenance requirements that can hardly be met in a manufacturing environment. To address these limitations, we propose a non-destructive, economic, and robust woodchip MC sensing approach utilizing channel state information (CSI) from industrial Internet-of-Things (IIoT) based Wi-Fi. While these IIoT devices are small, low-cost, and rugged to stand for harsh environment, they do have their limitations such as the raw CSI data are often very noisy and sensitive to woodchip packing. Thus, direct application of machine learning (ML) algorithms leads to poor performance. To address this, statistics pattern analysis (SPA) is utilized to extract physically and statistically meaningful features from the raw CSI data, which are sensitive to woodchip MC but not to packing. The SPA features are then used for developing multiclass classification models as well as regression models using various linear and nonlinear ML techniques to provide potential solutions to woodchip MC estimation for the pulp and paper industry. 

Rotating machines, such as pumps and compressors, are critical components in refineries and chemical plants used to transport fluids between processing units. Bearings are often the critical parts of rotating machinery, and their failure could result in economic loss and/or safety issues. Therefore, estimation of the remaining useful life (RUL) of a bearing plays an important role in reducing production losses and avoiding machine damage. Because bearing failure mechanisms tend to be complex and stochastic, data-driven RUL estimation approaches have found more applications. This work proposes a novel RUL estimation method based on systematic feature engineering and extreme learning machine (ELM). The PRONOSTIA dataset is used to demonstrate the effectiveness of the proposed method. 

The democratization of data is transforming our world. Together with the advances in computer and engineering technology, these advancements drive the rapid change in the landscape of jobs and work. There are many reports indicating that industry finds itself constrained by today’s relatively small supply of well-trained data science talent, and hiring demand for data scientists has begun to increase rapidly; some projections forecast that approximately 2.7 million new data science positions will be available by 2020. Unsurprisingly, the data science and engineering (DSE) programs across the nation have grown significantly in the past a few years. DSE education requires both appropriate classwork and hands-on experience with real data and real applications. While significant progress has been made in the former, one key aspect that yet to be addressed is hands-on experience incorporating real-world applications. In this work, we will review the efforts that explore real data and application based data science education. 

The democratization of data is transforming our world. Together with the advances in computer and engineering technology, these advancements drive the rapid change in the landscape of jobs and work. There are many reports indicating that industry finds itself constrained by today’s relatively small supply of well-trained data science talent, and hiring demand for data scientists has begun to increase rapidly; some projections forecast that approximately 2.7 million new data science positions will be available by 2020. Unsurprisingly, the data science and engineering (DSE) programs across the nation have grown significantly in the past a few years. DSE education requires both appropriate classwork and hands-on experience with real data and real applications. While significant progress has been made in the former, one key aspect that yet to be addressed is hands-on experience incorporating real-world applications. In this work, we will review the efforts that explore real data and application based data science education.