Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

Peer reviewedPostprin

Thermography-based methodology for multifault diagnosis on kinematic chain

The procedures for condition monitoring of electromechanical systems are undergoing a reformulation, mainly, due to the current thermographic affordability of infrared cameras to be incorporated in industrial applications. However, high-performing multifault data-driven methodologies must be investigated in order to infer reliable condition information from the thermal distribution of not only electrical motors but also of shafts and couplings. To address this issue, a novel thermography-based methodology is proposed. First, the infrared capture is processed to obtain a thermographic residual image of the kinematic chain. Second, the thermal distribution of the image's regions of interest is characterized by means of statistical features. Finally, a distributed self-organizing map structure is used to model the nominal thermal distribution to subsequently perform a fault detection and identification. The method provides a reliability quantification of the resulting condition assessment in order to avoid misclassifications and identify the actual fault root-causes. The performance and the effectiveness of the proposed methodology is validated experimentally and compared with the classical maximum temperature gradient procedure.Peer ReviewedPostprint (published version

Automated Species Classification Methods for Passive Acoustic Monitoring of Beaked Whales

The Littoral Acoustic Demonstration Center has collected passive acoustic monitoring data in the northern Gulf of Mexico since 2001. Recordings were made in 2007 near the Deepwater Horizon oil spill that provide a baseline for an extensive study of regional marine mammal populations in response to the disaster. Animal density estimates can be derived from detections of echolocation signals in the acoustic data. Beaked whales are of particular interest as they remain one of the least understood groups of marine mammals, and relatively few abundance estimates exist. Efficient methods for classifying detected echolocation transients are essential for mining long-term passive acoustic data. In this study, three data clustering routines using k-means, self-organizing maps, and spectral clustering were tested with various features of detected echolocation transients. Several methods effectively isolated the echolocation signals of regional beaked whales at the species level. Feedforward neural network classifiers were also evaluated, and performed with high accuracy under various noise conditions. The waveform fractal dimension was tested as a feature for marine biosonar classification and improved the accuracy of the classifiers. [This research was made possible by a grant from The Gulf of Mexico Research Initiative. Data are publicly available through the Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC) at https://data.gulfresearchinitiative.org.] [DOIs: 10.7266/N7W094CG, 10.7266/N7QF8R9K

Multi-Attribute Seismic Analysis Using Unsupervised Machine Learning Method: Self-Organizing Maps

Seismic attributes are a fundamental part of seismic interpretation and are routinely used by geoscientists to extract key information and visualize geological features. By combining different findings from each attribute, they can provide a good insight of the area and help overcome many geological challenges. However, individually analyzing multiple attributes to find relevant information can be time-consuming and inefficient, especially when working with large datasets. It can lead to miscalculations, errors in judgement and human bias. This is where Machine Learning (ML) methods can be implemented to improve existing interpretations or find additional information. ML can help by handling large volumes of multi-dimensional data and interrelating them. Methods such as Self Organizing Maps (SOM) allow multi-attribute analysis and help extract more information as compared to quantitative interpretation. SOM is an unsupervised neural network that can find meaningful and reliable patterns corresponding to a specific geological feature (Roden and Chen, 2017). The purpose of this thesis was to understand how SOM can help make interpretations of direct hydrocarbon indicators (DHI) in the Statfjord Field area easier. Several AVO attributes were generated to detect DHIs and were then used as input for multi-attribute SOM analysis. SOMPY package in Python was used to train the model and generate SOM classification results. Data samples were classified based on BMU hits and clusters in the data. The classification was then applied to the whole dataset and converted to seismic sections for comparison and interpretation. SOM classified seismic lines were compared with the results of the AVO attributes. Since DHIs are anomalous data, they were expected to be represented by small data clusters and BMUs with low hits. While SOM reproduced the seismic reflectors well, it did not define the DHI features clearly for them to be easily interpreted. Use of fewer seismic attributes and computational limitations of the machine could be some of the reasons behind not achieving desired results. However, the study has room for improvement and the potential to produce meaningful results. Improvements in model design and training, and also the selection of input attributes are some of the areas that need to be addressed. Furthermore, testing other Python libraries and better handling of large datasets can allow better performance and more accurate results

The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases

One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs

Clustering time series: an application to COVID-19 data

In this paper we present an attempt of clustering time series focusing on Italian data about COVID-19. From the methodological point of view, we first present a review of the most important methods existing in literature for time series clustering. Similarly to cross-sectional clustering, time series clustering moves from the choice of an opportune algorithm to produce clusters. Several algorithms have been developed to carry out time series clustering and the choice of which one is more adapt depends on both the aim of the analysis itself and the typology of data at hand. We apply some of these methods to the data set of daily time series on intensive care and deaths for COVID19 stretching from, respectively, 23/02/2020 to 15/02/2022 and from 23/02/2020 to 29/03/2022. These data refer to the 19 Italian regions and the two autonomous provinces of Trento and Bolzano

Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote

The fault classification of a small sample of high dimension is challenging, especially for a nonlinear and non-Gaussian manufacturing process. In this paper, a similarity-based feature selection and sub-space neighbor vote method is proposed to solve this problem. To capture the dynamics, nonlinearity, and non-Gaussianity in the irregular time series data, high order spectral features, and fractal dimension features are extracted, selected, and stacked in a regular matrix. To address the problem of a small sample, all labeled fault data are used for similarity decisions for a specific fault type. The distances between the new data and all fault types are calculated in their feature subspaces. The new data are classified to the nearest fault type by majority probability voting of the distances. Meanwhile, the selected features, from respective measured variables, indicate the cause of the fault. The proposed method is evaluated on a publicly available benchmark of a real semiconductor etching dataset. It is demonstrated that by using the high order spectral features and fractal dimensionality features, the proposed method can achieve more than 84% fault recognition accuracy. The resulting feature subspace can be used to match any new fault data to the fingerprint feature subspace of each fault type, and hence can pinpoint the root cause of a fault in a manufacturing process

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review