The roller bearings are widely used in aviation cargo systems, engines, agriculture, heavy equipment and machinery, solar panels, medical equipment, automobile industry, powerhouses, and many others. Bearing faults during the operation process will result in downtime, economic loss, and even human injury. To prevent these from happening, rolling bearing fault diagnosis has become a mature discipline. Deep learning networks have been known as effective methods for bearing fault diagnoses. Deep learning neural networks such as the convolutional neural network (CNN) use the images as inputs. In contrast, the others, such as long-short term memory (LSTM), may apply data sequences as inputs. <br>This thesis research work focuses on performance evaluations of deep learning networks according to the classification accuracy by utilizing various signal transforms to form the network inputs. CNN and LSTM are adopted as our deep learning network structures. Besides raw data, the algorithms for processing input signals include short-time Fourier transform (STFT), Cepstrum, wavelet packet transform (WPT), and empirical mode decomposition (EMD). In addition, this paper also applies three commonly used machine learning algorithms for comparison, namely K nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). Finally, a one-dimensional CNN structure is designed and implemented.<br>Our simulations validate the effectiveness for each network input formulation based on the Case Western Reserve University (CWRU) bearing dataset. <br>

Mathematics has been singled out as a challenging discipline to teach fully online (FO). Yet both the demand for and development of FO mathematics courses is increasing with little known about the quality of these courses and many calling for research. Whereas most research has investigated the nature of these courses by examining instructional outputs such as student grades this research seeks the same insight but by examining instructional inputs. Specifically, it seeks to investigate the nature of current assessment practice in FO mathematics courses. To conduct this investigation, deep learning (Marton & S??lj??, 1976a, 1976b) is used as the principle theoretical framework. From the growing body of literature associated with deep learning, two studies are selected to investigate current FO mathematics instructors assessment practices. An additional framework based on empirical findings related to the use of different kinds of feedback is also used. In total, six study measures are used to conduct a mixed methods study in two parts. The target demographic and course context are tertiary instructors from Western nations that teach introductory level mathematics (particularly statistics and calculus). The first study explores current FO mathematics assessment practices using an online survey (n=70) where the majority of participants originate from US higher education institutions. In the second study six of the US survey participants are interviewed about how their assessment practices and approaches used in their FO mathematics courses differ from those used in their face-to-face (F2F) mathematics courses. This study represents the first known attempt to investigate the nature of tertiary FO mathematics instructors assessment practices using appropriate theoretical frameworks. In particular, it investigates mathematics instructors experiences of the affordances and constraints of the FO course context when adapting their F2F practice to this new environment. Findings suggest the FO course context is a challenging environment for instructors to orient their teaching and assessment practice in a way that helps develop students understanding of mathematics. Analysis of interview responses suggests the problem lies with the nature of interactivity provided in the FO course context.