Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

Physiological complexity of EEG as a proxy for dementia risk prediction: a review and preliminary cross-section analysis

The aim of this work is to give the readers a review (perspective) of prior work on this kind of complexity-based detection from resting-state EEG and present our preliminary cross-section analysis results on how EEG complexity of supposedly healthy senior persons can serve as an early warning to clinicians. Together with the use of wearables for health, this approach to early detection can be done out of clinical setting improving the chances of increasing the quality of life in seniors.Comment: 19 pages, 1 figure, 1 tabl

Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis

Multivariate Entropy quantification algorithms are becoming a prominent tool for the extraction of information from multi-channel physiological time-series. However, in the analysis of physiological signals from heterogeneous organ systems, certain channels may overshadow the patterns of others, resulting in information loss. Here, we introduce the framework of Stratified Entropy to prioritize each channels' dynamics based on their allocation to respective strata, leading to a richer description of the multi-channel time-series. As an implementation of the framework, three algorithmic variations of the Stratified Multivariate Multiscale Dispersion Entropy are introduced. These variations and the original algorithm are applied to synthetic time-series, waveform physiological time-series, and derivative physiological data . Based on the synthetic time-series experiments, the variations successfully prioritize channels following their strata allocation while maintaining the low computation time of the original algorithm. In experiments on waveform physiological time-series and derivative physiological data, increased discrimination capacity was noted for multiple strata allocations in the variations when benchmarked to the original algorithm. This suggests improved physiological state monitoring by the variations. Furthermore, our variations can be modified to utilize a priori knowledge for the stratification of channels. Thus, our research provides a novel approach for the extraction of previously inaccessible information from multi-channel time series acquired from heterogeneous systems

Voronoi Decomposition of Cardiovascular Dependency Structures in Different Ambient Conditions: An Entropy Study

This paper proposes a method that maps the coupling strength of an arbitrary number of signals D, D >= 2, into a single time series. It is motivated by the inability of multiscale entropy to jointly analyze more than two signals. The coupling strength is determined using the copula density defined over a [0 1](D) copula domain. The copula domain is decomposed into the Voronoi regions, with volumes inversely proportional to the dependency level (coupling strength) of the observed joint signals. A stream of dependency levels, ordered in time, creates a new time series that shows the fluctuation of the signals' coupling strength along the time axis. The composite multiscale entropy (CMSE) is then applied to three signals, systolic blood pressure (SBP), pulse interval (PI), and body temperature (t(B)), simultaneously recorded from rats exposed to different ambient temperatures (t(A)). The obtained results are consistent with the results from the classical studies, and the method itself offers more levels of freedom than the classical analysis

Medical Diagnosis with Multimodal Image Fusion Techniques

Image Fusion is an effective approach utilized to draw out all the significant information from the source images, which supports experts in evaluation and quick decision making. Multi modal medical image fusion produces a composite fused image utilizing various sources to improve quality and extract complementary information. It is extremely challenging to gather every piece of information needed using just one imaging method. Therefore, images obtained from different modalities are fused Additional clinical information can be gleaned through the fusion of several types of medical image pairings. This study's main aim is to present a thorough review of medical image fusion techniques which also covers steps in fusion process, levels of fusion, various imaging modalities with their pros and cons, and  the major scientific difficulties encountered in the area of medical image fusion. This paper also summarizes the quality assessments fusion metrics. The various approaches used by image fusion algorithms that are presently available in the literature are classified into four broad categories i) Spatial fusion methods ii) Multiscale Decomposition based methods iii) Neural Network based methods and iv) Fuzzy Logic based methods. the benefits and pitfalls of the existing literature are explored and Future insights are suggested. Moreover, this study is anticipated to create a solid platform for the development of better fusion techniques in medical applications

Estimating Air Pollution Levels Using Machine Learning

Air pollution has emerged as a substantial concern, especially in developing countries worldwide. An important aspect of this issue is the presence of PM2.5. Air pollutants with a diameter of 2.5 or less micrometers are known as PM2.5. Due to their size, these particles are a serious health risk and can quickly infiltrate the lungs, leading to a variety of health problems. Due to growing concerns about air pollution, technology like automatic air quality measurement can offer beneficial assistance for both personal and business decisions. This research suggests an ensemble machine learning model that can efficiently replace the standard air quality estimation techniques, which need several instruments and setup and have large financial expenditures for equipment acquisition and maintenance

Thermo-mechano-chemical modeling and computation of thermosetting polymers used in post-installed fastening systems in concrete structures

As thermoset polymers find frequent implementation in engineering design, their application in structural engineering is rather limited. One key reason relies on the ongoing curing process in typical applications such as post-installed adhesive anchors, joints by structural elements or surface-mounted laminates glued by adhesive polymers. Mechanochemistry including curing and aging under thermal as well as mechanical loading causes a multiphysics problem to be discussed. For restricting the variety of material models based on empirical observations, we aim at a thermodynamically sound strategy for modeling thermosets. By providing a careful analysis and clearly identifying the assumptions and simplifications, we present the general framework for modeling and computational implementation of thermo-mechano-chemical processes by using open-source codes

Ultrasonic Analysis and Tools for Quantitative Material State Awarness of Engineered Materials

The objective of this research is to devise new methods and tools to generate real time awareness of the material state of composite and metallic structures through ultrasonic nondestructive evaluation (NDE) and structural health monitoring (SHM) at its very early stage of failure. To device new methodology it is also important to verify the method through virtual experiments and hence computational NDE is getting popular in the recent years. In this thesis, while experimental methodology is developed to understand the material state at its early stage of failure, a new peridynamic based Peri-Elastodynamic (PED) computational method is also developed for virtual NDE and SHM experiments. In the experimental part, material state awareness through precursor damage quantification is proposed for composite materials and in the predictive part modelling of ultrasonic wave propagation in the engineered materials is developed. Symbiotic information fusion between the Guided Coda Wave Interferometry (CWI) and Quantitative Ultrasonic Image Correlation (QUIC) was devised for the awareness and the quantification of the precursor damage state in composites. The proposed research work is divided into two major parts a) Experimental and b) Computational. a) Experimental: In composite materials, the precursor damages (for example matrix cracking, microcracks, voids, fiber micro-buckling, local fiber breakage, local debonding, etc.) are insensitive to the low-frequency ultrasonic NDE or Structural Health Monitoring (SHM) (~100–~500 kHz) methods. Overcoming this barrier, an online method using the later part of the guided wave signal, which is often neglected is proposed for the precursor damage quantification. Although the first-arrival wave packets that contain the fundamental guided Lamb wave modes are unaltered, the following part of the wave packets however carry significant information about the precursor events with predictable phase shifts. The Cross-correlation and Taylor-series-based modified CWI technique is proposed to quantify the stretch parameter to compensate the phase shifts in the coda wave as a result of precursor damage in composites. The results are thoroughly validated with newly formulated high frequency (\u3e~25MHz) QUIC method. The proposed process is validated and verified with American Society of Testing of Materials (ASTM) standards woven composite-fiber-reinforced-laminate specimens (CFRP). Both online CWI and offline QUIC was performed to prove the feasibility and reliability of the proposed precursor damage quantification process. Visual proof of the precursor events is provided from the digital micro optical microscopy and scanning electron microscopy. Additionally, acoustic-nonlinearity of analysis Lamb wave propagation was employed to investigate, stress-relaxation phenomena in composites. Fatigue loading on composite specimens followed by relaxation experiments were conducted to examine influence of damage and relaxation on acoustic-nonlinearity. It was observed that the stress-relaxation in composite is primarily coupled with the second-order nonlinearity parameters derived from the Lamb wave modes. Furthermore, these parameters were found inherently associated with the remaining strength of the composites. Results from the nonlinear analysis were found to be in good agreement with those obtained from CWI analysis. In the near future, it is expected that the structure, structural component or individual material states could be digitally certified for their future missions by including a predictive tool in a “Digital Twin” software fusing the information from experimental finding. This thesis contributes to this concept and the information obtained from experimental NDE discussed above can be utilized by a predictive tool to predict accurate material behavior as well as NDE or SHM sensor signals off-line, simultaneously. Considering multiple advantages of peridynamic based approach in incorporating experimental data and damage modelling capability over tradition approaches, newly devised Peri-Elastodynamic (PED) is discussed in the following paragraph to simulate the three-dimensional (3D) Lamb wave modes in materials for the first time. b) Computational: PED is a nonlocal meshless approach which is a scale-independent generalized technique to visualize the acoustic and ultrasonic waves in plate-like structures. Characteristics of the fundamental Lamb wave modes are simulated in a plate-like structure with a surface mounted piezoelectric (PZT) transducer which is actuated from the top surface. In addition, guided ultrasonic wave modes were also simulated in a damaged plate. the PED results were validated with the experimental results which shows that the newly developed method is more accurate and computationally cheaper than the FEM to be used for computational NDE and SHM. PED was also extended to investigate the wave-damage interaction with damage (e.g., a crack) in the plate. The accuracy of the proposed technique herein is confirmed by performing the error analysis on symmetric and anti-symmetric Lamb wave modes compared to the experimental results for both pristine and damaged plat