1,142 research outputs found

    Algorithms for Fault Detection and Diagnosis

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    Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

    An improved packing equivalent circuit modeling method with the cell-to-cell consistency state evaluation of the internal connected lithium-ion batteries.

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    The existing equivalent modeling methods reported in literature focuses mainly on the battery cells and do not take the packing consistency state into consideration, which exists on the internal connected cells of the lithium-ion battery pack. An improved equivalent circuit model is constructed and reported in this manuscript for the first time, which can be used to characterize the working characteristics of the packing lithium-ion batteries. A new equilibrium concept named as state of balance is proposed as well as the calculation process, which is realized by considering the real-time detected internal battery cell voltages. In addition, this new equilibrium concept aims to obtain more information on the real-time consistency characterization of the battery pack. The improved adaptive equivalent circuit model is investigated by using the improved splice modeling method, in which the statistical noise properties are corrected and the additional parallel resistance-capacitance circuit is introduced. The parameter correction treatment is carried out by comparing the estimated and experimental detected closed circuit voltages. Furthermore, the tracking error is found to be 0.005 V and accounts for 0.119% of the nominal battery voltage. By taking the packing consistency state and temperature correction into consideration, the accurate working characteristic expression is realized in the improved equivalent circuit modeling process. Finally, the model proposed in this manuscript presents a great number of advantages compared to other methods reported so far, like has the high accuracy, and the ability to protect the security of the lithium-ion battery pack in the power supply application

    A Study of Iron-Nitrogen-Carbon Fuel Cell Catalysts: Chemistry – Nanostructure – Performance

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    Fuel cells have the potential to be a pollution-free, low-cost, and energy efficient alternative to the internal combustion engine for transportation and small-scale stationary power applications. The current state of fuel cell technology has already achieved two of these three lofty goals. The remaining barrier to wide-scale deployment is the high cost, which is primarily caused by dependence on large amounts of platinum to catalyze the energy conversion reactions. To overcome this barrier and facilitate the integration of fuel cells into mainstream applications, research into a new class of catalyst materials that do not require platinum is needed. There has been a significant amount of research effort directed toward the development of platinum-group metal free (PGM-free) catalysts, yet there is a lack of consensus on both the engineering parameters necessary to improve the technology and the fundamental science that would facilitate rational design. I have engaged in research on PGM-free catalysts based on inexpensive and abundant reagents, specifically: nicarbazin and iron. Catalysts made from these precursors have previously proven to be among the best PGM-free catalysts, but their continued advancement suffered from the same lack of understanding that besets all catalysts in this class. The work I have performed address both engineering concerns and fundamental underlying principles. I present results demonstrating correlations between physical structure, chemical speciation, and synthesis parameters, as well as addressing active site chemistry and likely locations. My research presented herein introduces new morphology analysis techniques and elucidates several key structure-to-property characteristics of catalysts derived from iron and nicarbazin. I discuss the development and application of a new length-scale specific surface analysis technique that allows for analysis of well-defined size ranges from a few nm to several microns. The existing technique of focused ion beam tomography is modified and optimized for platinum-group metal free catalyst layers, facilitating direct observation of catalyst integration into catalyst layers. I present evidence supporting the hypothesis that atomically dispersed iron coordinated with nitrogen are the dominant active sites in these catalysts. Further, that the concentration of surface oxides in the carbon structure, which can be directly influenced by synthesis parameters, correlates with both the concentration of active sites in the material and with fuel cell performance. Catalyst performance is hindered by the addition of carbon nanotubes and by the presence of metallic iron. Evidence consistent with the catalytic active sites residing in the graphitic plane is also presented

    Combining Low-dimensional Wavelet Features and Support Vector Machine for Arrhythmia Beat Classification

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    Automatic feature extraction and classification are two main tasks in abnormal ECG beat recognition. Feature extraction is an important prerequisite prior to classification since it provides the classifier with input features, and the performance of classifier depends significantly on the quality of these features. This study develops an effective method to extract low-dimensional ECG beat feature vectors. It employs wavelet multi-resolution analysis to extract time-frequency domain features and then applies principle component analysis to reduce the dimension of the feature vector. In classification, 12-element feature vectors characterizing six types of beats are used as inputs for one-versus-one support vector machine, which is conducted in form of 10-fold cross validation with beat-based and record-based training schemes. Tested upon a total of 107049 beats from MIT-BIH arrhythmia database, our method has achieved average sensitivity, specificity and accuracy of 99.09%, 99.82% and 99.70%, respectively, using the beat-based training scheme, and 44.40%, 88.88% and 81.47%, respectively, using the record-based training scheme

    A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction.

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    A comprehensive working state monitoring method is proposed to protect the power lithium-ion battery packs, implying accurate estimation effect but using minimal time demand of self-learning treatment. A novel state of charge estimation model is conducted by using the improved unscented Kalman filtering method, in which the state of balance and aging process correction is considered, guaranteeing the powered battery supply reliability effectively. In order to realize the equilibrium state evaluation among the internal battery cells, the numerical description and evaluation is putting forward, in which the improved variation coefficient is introduced into the iterative calculation process. The intermittent measurement and real-time calibration calculation process is applied to characterize the capacity change of the battery pack towards the cycling maintenance number, according to which the aging process impact correction can be investigated. This approach is different to the traditional methods by considering the multi-input parameters with real-time correction, in which every calculation step is investigated to realize the working state estimation by using the synthesis algorithm. The state of charge estimation error is 1.83%, providing the technical support for the reliable power supply application of the lithium-ion battery packs

    Engine defect detection using wavelet analysis.

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    Development of a Plasmonic On-Chip System to Characterize Changes from External Perturbations in Cardiomyocytes

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    Today’s heart-on-a-chip devices are hoped to be the state-of-the-art cell and tissue characterizing tool, in clinically applicable regenerative medicine and cardiac tissue engineering. Due to the coupled electromechanical activity of cardiomyocytes (CM), a comprehensive heart-on-a-chip device as a cell characterizing tool must encompass the capability to quantify cellular contractility, conductivity, excitability, and rhythmicity. This dissertation focuses on developing a successful and statistically relevant surface plasmon resonance (SPR) biosensor for simultaneous recording of neonatal rat cardiomyocytes’ electrophysiological profile and mechanical motion under normal and perturbed conditions. The surface plasmon resonance technique can quantify (1) molecular binding onto a metal film, (2) bulk refractive index changes of the medium near (nm) the metal film, and (3) dielectric property changes of the metal film. We used thin gold metal films (also called chips) as our plasmonic sensor and obtained a periodic signal from spontaneously contracting CMs on the chip. Furthermore, we took advantage of a microfluidic module for controlled drug delivery to CMs on-chip, inhibiting and promoting their signaling pathways under dynamic flow. We identified that ionic channel activity of each contraction period of a live CM syncytium on a gold metal sensor would account for the non-specific ion adsorption onto the metal surface in a periodic manner. Moreover, the contraction of cardiomyocytes following their ion channel activity displaces the medium, changing its bulk refractive index near the metal surface. Hence, the real-time electromechanical activity of CMs using SPR sensors may be extracted as a time series we call the Plasmonic Cardio-Eukaryography Signal (P-CeG). The P-CeG signal render opportunities, where state-of-the-art heart-on-a-chip device complexities may subside to a simpler, faster and cheaper platform for label-free, non-invasive, and high throughput cellular characterization

    Feature Detection Techniques for Preprocessing Proteomic Data

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    Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease. The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) are substantial problems that need to be addressed, because any large-level data analyses are contingent on appropriate and statistically sound low-level procedures. Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations. Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information. In this paper, we focus on recent advances in feature detection as a tool for preprocessing proteomic data. This work highlights existing and newly developed feature detection algorithms for proteomic datasets, particularly relating to time-of-flight mass spectrometry, and two-dimensional gel electrophoresis. Note, however, that the associated data structures (i.e., spectral data, and images containing spots) used as input for these methods are obtained via all gel-based and nongel-based methods discussed in this manuscript, and thus the discussed methods are likewise applicable

    Advances in Neural Signal Processing

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    Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
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