Machine Learning Applications for Thermal Manufacturing Processes

This thesis introduces a novel approach for the extraction of physically meaningful thermal component time series during the manufacturing of casting parts. I treat their extraction as Blind Source Separation (BSS) problem by exploiting process-related prior knowledge. The proposed method arranges temperature time series into a data matrix, which is then decomposed by Non-negative Matrix Factorization (NMF). The latter is guided by a knowledge-based strategy, which initializes the NMF component matrix with time curves designed according to basic physical processes. It is shown how to extract components linked to physical phenomena that typically occur during production and cannot be monitored directly. The proposed methods are applied to real world data, collected in a foundry during the series production of casting parts for the automobile industry

Big Data Analytics and Information Science for Business and Biomedical Applications II

The analysis of big data in biomedical, business and financial research has drawn much attention from researchers worldwide. This collection of articles aims to provide a platform for an in-depth discussion of novel statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions to these areas are showcased

Time Series Modelling

The analysis and modeling of time series is of the utmost importance in various fields of application. This Special Issue is a collection of articles on a wide range of topics, covering stochastic models for time series as well as methods for their analysis, univariate and multivariate time series, real-valued and discrete-valued time series, applications of time series methods to forecasting and statistical process control, and software implementations of methods and models for time series. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples

A study of information-theoretic metaheuristics applied to functional neuroimaging datasets

This dissertation presents a new metaheuristic related to a two-dimensional ensemble empirical mode decomposition (2DEEMD). It is based on Green’s functions and is called Green’s Function in Tension - Bidimensional Empirical Mode Decomposition (GiT-BEMD). It is employed for decomposing and extracting hidden information of images. A natural image (face image) as well as images with artificial textures have been used to test and validate the proposed approach. Images are selected to demonstrate efficiency and performance of the GiT-BEMD algorithm in extracting textures on various spatial scales from the different images. In addition, a comparison of the performance of the new algorithm GiT-BEMD with a canonical BEEMD is discussed. Then, GiT-BEMD as well as canonical bidimensional EEMD (BEEMD) are applied to an fMRI study of a contour integration task. Thus, it explores the potential of employing GiT-BEMD to extract such textures, so-called bidimensional intrinsic mode functions (BIMFs), of functional biomedical images. Because of the enormous computational load and the artifacts accompanying the extracted textures when using a canonical BEEMD, GiT-BEMD is developed to cope with such challenges. It is seen that the computational cost is decreased dramatically, and the quality of the extracted textures is enhanced considerably. Consequently, GiT-BEMD achieves a higher quality of the estimated BIMFs as can be seen from a direct comparison of the results obtained with different variants of BEEMD and GiT-BEMD. Moreover, results generated by 2DBEEMD, especially in case of GiT-BEMD, distinctly show a superior precision in spatial localization of activity blobs when compared with a canonical general linear model (GLM) analysis employing statistical parametric mapping (SPM). Furthermore, to identify most informative textures, i.e. BIMFs, a support vector machine (SVM) as well as a random forest (RF) classifier is employed. Classification performance demonstrates the potential of the extracted BIMFs in supporting decision making of the classifier. With GiT-BEMD, the classification performance improved significantly which might also be a consequence of a clearer structure for these modes compared to the ones obtained with canonical BEEMD. Altogether, there is strong believe that the newly proposed metaheuristic GiT-BEMD offers a highly competitive alternative to existing BEMD algorithms and represents a promising technique for blindly decomposing images and extracting textures thereof which may be used for further analysis

Fault Diagnosis of Rotating Machinery using Improved Entropy Measures

Fault diagnosis of rotating machinery is of considerable significance to ensure high reliability and safety in industrial machinery. The key to fault diagnosis consists in detecting potential incipient fault presence, recognizing fault patterns, and identifying degrees of failures in machinery. The process of data-driven fault diagnosis method often requires extracting useful feature representations from measurements to make diagnostic decision-making. Entropy measures, as suitable non-linear complexity indicators, estimate dynamic changes in measurements directly, which are challenging to be quantified by conventional statistical indicators. Compared to single-scale entropy measures, multiple-scale entropy measures have been increasingly applied to time series complexity analysis by quantifying entropy values over a range of temporal scales. However, there exist a number of challenges in traditional multiple-scale entropy measures in analyzing bearing signals for bearing fault detection. Specifically, a large majority of multiple-scale entropy methods neglect high�frequency information in bearing vibration signal analysis. Moreover, the data length of transformed multiple signals is greatly reduced as scale factor increases, which can introduce incoherence and bias in entropy values. Lastly, non-linear and non-stationary behaviors of vibration signals due to interference and noise may reduce the diagnostic performance of traditional entropy methods in bearing health identification, especially in complex industrial settings. This dissertation proposes a novel multiple-scale entropy measure, named Adaptive Multiscale Weighted Permutation Entropy (AMWPE), for extracting fault features associated with complexity change in bearing vibration analysis. A new scale-extraction mechanism - adaptive Fine-to-Coarse (F2C) procedure - is presented to generate multiple-scale time series from the original signal. It has advantages of extracting low- and high-frequency information from measurements and generating improved multiple-scale time series with a hierarchical structure. Numerical evaluation is carried out to study the performance of the AMWPE measure in analyzing the complexity change of synthetic signals. Results demonstrated that the AMWPE algorithm could provide high consistency and stable entropy values in entropy estimation. It also presents high robustness against noise in analyzing noisy bearing signals in comparison with traditional entropy methods. Additionally, a new bearing diagnosis method is put forth, where the AMWPE method is applied for entropy analysis and a multi-class support vector machine classifier is used for identifying bearing fault patterns, respectively. Three experimental case studies are carried out to investigate the effectiveness of the proposed diagnosis method for bearing diagnosis. Comparative studies are presented to compare the diagnostic performance of the proposed entropy method and traditional entropy methods in terms of computational time of entropy estimation, feature representation, and diagnosis accuracy rate. Further, noisy bearing signals with different signal-to-noise ratios are analyzed using various entropy measures to study their robustness against noise in bearing diagnosis. Additionally, the developed adaptive F2C procedure can be extended to a variety of entropy algorithms based on improved single-scale entropy method used in entropy estimation. In the combination of artificial intelligence techniques, the improved entropy algorithms are expected to apply to machine health conditions and intelligent fault diagnosis in complex industrial machinery. Besides, they are suitable to evaluate the complexity and irregularity of other non-stationary signals measured from non-linear systems, such as acoustic emission signals and physiological signals

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Advanced methods for analysing and modelling multivariate palaeoclimatic time series

The separation of natural and anthropogenically caused climatic changes is an important task of contemporary climate research. For this purpose, a detailed knowledge of the natural variability of the climate during warm stages is a necessary prerequisite. Beside model simulations and historical documents, this knowledge is mostly derived from analyses of so-called climatic proxy data like tree rings or sediment as well as ice cores. In order to be able to appropriately interpret such sources of palaeoclimatic information, suitable approaches of statistical modelling as well as methods of time series analysis are necessary, which are applicable to short, noisy, and non-stationary uni- and multivariate data sets. Correlations between different climatic proxy data within one or more climatological archives contain significant information about the climatic change on longer time scales. Based on an appropriate statistical decomposition of such multivariate time series, one may estimate dimensions in terms of the number of significant, linear independent components of the considered data set. In the presented work, a corresponding approach is introduced, critically discussed, and extended with respect to the analysis of palaeoclimatic time series. Temporal variations of the resulting measures allow to derive information about climatic changes ...thesi

Stock Market Investment Using Machine Learning

Genetic Algorithm-Support Vector Regression (GA-SVR) and Random Forest Regression (RFR) were constructed to forecast stock returns in this research. 15 financial indicators were selected through fuzzy clustering from 42 financial indicators, then combined with 8 technical indicators as input space, the 10-day stocks return was used as labels. The results show that GA-SVR and RFR can make compelling forecasting and pass the robustness test. GA-SVR and RFR exhibit different processing preferences for features with different importance. Furthermore, by testing stock markets in China, Hong Kong (China) and the United States, the model shows different effectiveness