When applying a diagnostic technique to complex systems, whose dynamics, constraints, and environment evolve over time, being able to re-evaluate the residuals that are capable of detecting defaults and proposing the most appropriate ones can quickly prove to make sense. For this purpose, the concept of adaptive diagnosis is introduced. In this work, the contributions of information theory are investigated in order to propose a Fault-Tolerant multi-sensor data fusion framework. This work is part of studies proposing an architecture combining a stochastic filter for state estimation with a diagnostic layer with the aim of proposing a safe and accurate state estimation from potentially inconsistent or erroneous sensors measurements. From the design of the residuals, using α-Rényi Divergence (α-RD), to the optimization of the decision threshold, through the establishment of a function that is dedicated to the choice of α at each moment, we detail each step of the proposed automated decision-support framework. We also dwell on: (1) the consequences of the degree of freedom provided by this α parameter and on (2) the application-dictated policy to design the α tuning function playing on the overall performance of the system (detection rate, false alarms, and missed detection rates). Finally, we present a real application case on which this framework has been tested. The problem of multi-sensor localization, integrating sensors whose operating range is variable according to the environment crossed, is a case study to illustrate the contributions of such an approach and show the performance. 相似文献
A low-cost and straightforward hybrid NOA (Norland optical adhesive) 81-glass microchip electrophoresis device was designed and developed for protein separation using indirect fluorescence detection. This new microchip was first characterized in terms of surface charge density via electroosmotic mobility measurement and stability over time. A systematic determination of the electroosmotic mobility (μeo) over a wide pH range (2–10) and at various ionic strengths (20–50 mM) was developed for the first time via the neutral marker approach in an original simple frontal methodology. The evolution of μeo was proved consistent with the silanol and thiol functions arising from the glass and the NOA materials, respectively. The repeatability and reproducibility of the measurements on different microchips (RSD < 14%) and within 15 days (less than 5% decrease) were successfully demonstrated. The microchip was then applied for the efficient electrophoretic separation of proteins in a zonal mode coupled with indirect fluorescence detection, which is, to our knowledge, the first proof of concept of capillary zone electrophoresis in this hybrid microsystem. 相似文献
The detection of cancer biomarkers is of great significance for the early screening of cancer. Detecting the content of sarcosine in blood or urine has been considered to provide a basis for the diagnosis of prostate cancer. However, it still lacks simple, high-precision and wide-ranging sarcosine detection methods. In this work, a Ti3C2TX/Pt–Pd nanocomposite with high stability and excellent electrochemical performance has been synthesized by a facile one-step alcohol reduction and then used on a glassy carbon electrode (GCE) with sarcosine oxidase (SOx) to form a sarcosine biosensor (GCE/Ti3C2TX/Pt–Pd/SOx). The prominent electrocatalytic activity and biocompatibility of Ti3C2TX/Pt–Pd enable the SOx to be highly active and sensitive to sarcosine. Under the optimized conditions, the prepared biosensor has a wide linear detection range to sarcosine from 1 to 1000 µM with a low limit of detection of 0.16 µM (S/N = 3) and a sensitivity of 84.1 µA/mM cm2. Besides, the reliable response in serum samples shows its potential in the early diagnosis of prostate cancer. More importantly, the successful construction and application of the amperometric biosensor based on Ti3C2TX/Pt–Pd will provide a meaningful reference for detecting other cancer biomarkers. 相似文献
The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.
Naked-eye colored chemo dosimeter based on vanilline based conjugated sensor was synthesized and characterized. The main point of this paper is that the solvent also affects on selectivity of metals. Vanilline based conjugate sensor exhibited high selectivity and sensitivity for detection of Ferric ions (Fe+3) in all (both polar and nonpolar) solvents according to absorbance which can be observed by naked eye. The selectivity was more prominent in nonpolar or less polar solvent due to solubility factor of ions and sensor but not for polar. The detection of limit of the synthesized probes was shown up to 0.84 ppm. The dielectric constant of solvents affected on the complex formation of ligand with transition metal ions. A filter paper strip system was used for rapid monitoring of detection by color variation. 相似文献
Poly 1,8-Diaminonaphtahlene/cysteine (poly 1,8-DAN/Cys) combined with carbon black (CB) nanoparticles are proposed as an excellent sensor for the detection of nitrite ions. To design the electrocatalyst, a simple approach consisting on drop-casting method was applied to disperse carbon black on the surface of glassy carbon electrode, followed by the immobilization of cysteine on the surface of CB nanoparticles. The electrochemical polymerization of 1,8-Diaminonaphthalene was conducted in acidic medium by using cyclic voltammetry. The prepared hybrid material was denoted poly 1,8-DAN /Cys/CB. Several methods were used to characterize the structural and electrochemical behavior of the reported hybrid material including Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), amperometry and differential pulse voltammetry (DPV). The prepared electrode displayed an outstanding electroactivity towards nitrite ions reflected by an enhancement in the intensity of the current and a decrease of the charge transfer resistance. Poly 1,8-DAN/Cys/CB displayed an excellent sensing performance towards the detection of nitrite with a very low detection limit of 0.25 µM. Two linear ranges of 1–40 µM and 20–210 µM when using amperometry and differential pulse voltammetry (DPV) were obtained respectively. This work highlights the simple preparation of a polymeric film rich in amine and thiol groups for nitrite detection. 相似文献