Abstract A powerful tool for bearing time series feature extraction and classification is introduced that is computationally inexpensive, easy to implement and suitable for real-time applications. In this paper the proposed technique is applied to two rolling element bearing time series classification problems and shown that in some cases no data pre-processing, artificial neural network or nearest neighbour approaches are required. From the results obtained it is clear that for the specific applications considered, the proposed method performed as well as or better than alternative approaches based on conventional feature extraction.

<p>Roller bearings are the common components used in the mechanical systems for mechanical processing and production. The running state of roller bearings often determines the machining accuracy and productivity on a manufacturing line. Roller bearing failure may lead to the shutdown of production lines, resulting in serious economic losses. Therefore, the research on roller bearing fault diagnosis has a great value. This thesis research first proposes a method of signal frequency spectral resampling to tackle the problem of bearing fault detection at different rotating speeds using a single speed dataset for training the network such as the one dimensional convolutional neural network (1D CNN). Second, this research work proposes a technique to connect the graph structures constructed from spectral components of the different bearing fault frequency bands into a sparse graph structure, so that the fault identification can be carried out effectively through a graph neural network in terms of the computation load and classification rate. Finally, the frequency spectral resampling method for feature extraction is validated using our self-collected datasets. The performance of the graph neural network with our proposed sparse graph structure is validated using the Case Western Reserve University (CWRU) dataset as well as our self-collected datasets. The results show that our proposed method achieves higher bearing fault classification accuracy than those recently proposed by other researchers using machine learning approaches and neural networks.</p>

The objective of this research is to develop a bearing fault detection scheme for electric machines via stator current. A new method, called the stator current noise cancellation method, is proposed to separate bearing fault-related components in the stator current. This method is based on the concept of viewing all bearing-unrelated components as noise and defining the bearing detection problem as a low signal-to-noise ratio (SNR) problem. In this method, a noise cancellation algorithm based on Wiener filtering is employed to solve the problem. Furthermore, a statistical method is proposed to process the data of noise-cancelled stator current, which enables bearing conditions to be evaluated solely based on stator current measurements. A detailed theoretical analysis of the proposed methods is presented. Several online tests are also performed in this research to validate the proposed methods. It is shown in this work that a bearing fault can be detected by measuring the variation of the RMS of noise-cancelled stator current by using statistical methods such as the Statistical Process Control. In contrast to most existing current monitoring techniques, the detection methods proposed in this research are designed to detect generalized-roughness bearing faults. In addition, the information about machine parameters and bearing dimensions are not required in the implementation.