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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>

<p> Roller bearing is one of the machine industry’s common components. The roller bearing operation status is usually related to production efficiency. Failure of bearings during operation will cause downtime and severe economic losses. To prevent this situation, the proposal of effective bearing fault diagnosis methods has become a popular research topic. This thesis research first validates several popular bearing diagnosis methods based on signal processing and machine learning. Second, a novel signal feature extraction method called sparse wavelet packet transform (WPT) decomposition and a corresponding feature learning model called multi-scale and multi-rate convolutional neural network (MSMR-CNN) are proposed. Finally, the proposed method is verified using both Case Western Reserve University (CWRU) dataset and the self-collected dataset. The results demonstrate that our proposed MSMR-CNN method achieves higher performance of bearing fault classification accuracy in comparison with the methods which are recently proposed by the other researchers using machine learning and neural networks .</p>

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