Eddy current defect response analysis using sum of Gaussian methods

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics

Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information

The Symbolic Aggregate approXimation (SAX) is a very popular symbolic dimensionality reduction technique of time series data, as it has several advantages over other dimensionality reduction techniques. One of its major advantages is its efficiency, as it uses precomputed distances. The other main advantage is that in SAX the distance measure defined on the reduced space lower bounds the distance measure defined on the original space. This enables SAX to return exact results in query-by-content tasks. Yet SAX has an inherent drawback, which is its inability to capture segment trend information. Several researchers have attempted to enhance SAX by proposing modifications to include trend information. However, this comes at the expense of giving up on one or more of the advantages of SAX. In this paper we investigate three modifications of SAX to add trend capturing ability to it. These modifications retain the same features of SAX in terms of simplicity, efficiency, as well as the exact results it returns. They are simple procedures based on a different segmentation of the time series than that used in classic-SAX. We test the performance of these three modifications on 45 time series datasets of different sizes, dimensions, and nature, on a classification task and we compare it to that of classic-SAX. The results we obtained show that one of these modifications manages to outperform classic-SAX and that another one slightly gives better results than classic-SAX.Comment: International Conference on Modeling Decisions for Artificial Intelligence - MDAI 2020: Modeling Decisions for Artificial Intelligence pp 230-23

Feature-based Time Series Analytics

Time series analytics is a fundamental prerequisite for decision-making as well as automation and occurs in several applications such as energy load control, weather research, and consumer behavior analysis. It encompasses time series engineering, i.e., the representation of time series exhibiting important characteristics, and data mining, i.e., the application of the representation to a specific task. Due to the exhaustive data gathering, which results from the ``Industry 4.0'' vision and its shift towards automation and digitalization, time series analytics is undergoing a revolution. Big datasets with very long time series are gathered, which is challenging for engineering techniques. Traditionally, one focus has been on raw-data-based or shape-based engineering. They assess the time series' similarity in shape, which is only suitable for short time series. Another focus has been on model-based engineering. It assesses the time series' similarity in structure, which is suitable for long time series but requires larger models or a time-consuming modeling. Feature-based engineering tackles these challenges by efficiently representing time series and comparing their similarity in structure. However, current feature-based techniques are unsatisfactory as they are designed for specific data-mining tasks. In this work, we introduce a novel feature-based engineering technique. It efficiently provides a short representation of time series, focusing on their structural similarity. Based on a design rationale, we derive important time series characteristics such as the long-term and cyclically repeated characteristics as well as distribution and correlation characteristics. Moreover, we define a feature-based distance measure for their comparison. Both the representation technique and the distance measure provide desirable properties regarding storage and runtime. Subsequently, we introduce techniques based on our feature-based engineering and apply them to important data-mining tasks such as time series generation, time series matching, time series classification, and time series clustering. First, our feature-based generation technique outperforms state-of-the-art techniques regarding the accuracy of evolved datasets. Second, with our features, a matching method retrieves a match for a time series query much faster than with current representations. Third, our features provide discriminative characteristics to classify datasets as accurately as state-of-the-art techniques, but orders of magnitude faster. Finally, our features recommend an appropriate clustering of time series which is crucial for subsequent data-mining tasks. All these techniques are assessed on datasets from the energy, weather, and economic domains, and thus, demonstrate the applicability to real-world use cases. The findings demonstrate the versatility of our feature-based engineering and suggest several courses of action in order to design and improve analytical systems for the paradigm shift of Industry 4.0