🍩 Database of Original & Non-Theoretical Uses of Topology
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Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches (2023)
Bingnan Wang, Hiroshi Inoue, Makoto KanemaruAbstract
Fault detection using motor current signature analysis (MCSA) is attractive for industrial applications due to its simplicity with no additional sensor installation required. However current components associated with faults are often very subtle and much smaller than the supply frequency component, making it challenging to detect and quantify fault levels. In this paper, we present our work on quantitative eccentricity fault diagnosis technologies for electric motors, including physical-model approach using improved winding function theory, which can simulate motor dynamics under faulty conditions and agrees well with experiment data, and data-driven approach using topological data analysis (TDA), which can effectively differentiate signals measured at different eccentricity levels. The advantages and limitations of each approach is discussed. Both methods can be extended to the detection and quantification of other types of electric motor faults. -
Topological Data Analysis for Electric Motor Eccentricity Fault Detection (2022)
Bingnan Wang, Chungwei Lin, Hiroshi Inoue, Makoto KanemaruAbstract
In this paper, we develop topological data analysis (TDA) method for motor current signature analysis (MCSA), and apply it to induction motor eccentricity fault detection. We introduce TDA and present the procedure of extracting topological features from time-domain data that will be represented using persistence diagrams and vectorized Betti sequences. The procedure is applied to induction machine phase current signal analysis, and shown to be highly effective in differentiating signals from different eccentricity levels. With TDA, we are able to use a simple regression model that can predict the fault levels with reasonable accuracy, even for the data of eccentricity levels that are not seen in the training data. The proposed method is model-free, and only requires a small segment of time-domain data to make prediction. These advantages make it attractive for a wide range of fault detection applications. -
Identification of Key Features Using Topological Data Analysis for Accurate Prediction of Manufacturing System Outputs (2017)
Wei Guo, Ashis G. BanerjeeAbstract
Topological data analysis (TDA) has emerged as one of the most promising approaches to extract insights from high-dimensional data of varying types such as images, point clouds, and meshes, in an unsupervised manner. To the best of our knowledge, here, we provide the first successful application of TDA in the manufacturing systems domain. We apply a widely used TDA method, known as the Mapper algorithm, on two benchmark data sets for chemical process yield prediction and semiconductor wafer fault detection, respectively. The algorithm yields topological networks that capture the intrinsic clusters and connections among the clusters present in the data sets, which are difficult to detect using traditional methods. We select key process variables or features that impact the system outcomes by analyzing the network shapes. We then use predictive models to evaluate the impact of the selected features. Results show that the models achieve at least the same level of high prediction accuracy as with all the process variables, thereby, providing a way to carry out process monitoring and control in a more cost-effective manner.