Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

A control valve plays a very significant role in the stable and efficient working of a control loop for any process. In a fluid flow process, the probability of failure of a control valve may increase for many reasons pertaining to a flow process such as high pressures at the inlet, different properties of the liquid flowing through the pipe, mechanical issue related to a control valve, ageing, etc. A method to detect faults in the valve can lead to better stability of the control loop. In the proposed work, a technique is developed to determine the fault in a pneumatic control valve by analyzing the vibration data at the outlet of the valve. The fault diagnosis of the valve is carried out by analyzing the change in vibration of the pipe due to the change in flow pattern induced by the control valve. The faults being considered are inflow and insufficient supply pressure faults. Vibration data obtained is processed using a signal processing technique like amplification, Fourier transform, etc. The support vector machine (SVM) algorithm is used to classify the vibration data into two classes, one normal and the other faulty. The designed algorithm is trained to identify faults and subjected to test with a practical setup; test results show an accuracy of 97%

Accurate Liquid Level Measurement with Minimal Error: A Chaotic Observer Approach

This paper delves into precisely measuring liquid levels using a specific methodology with diverse real-world applications such as process optimization, quality control, fault detection and diagnosis, etc. It demonstrates the process of liquid level measurement by employing a chaotic observer, which senses multiple variables within a system. A three-dimensional computational fluid dynamics (CFD) model is meticulously created using ANSYS to explore the laminar flow characteristics of liquids comprehensively. The methodology integrates the system identification technique to formulate a third-order state–space model that characterizes the system. Based on this mathematical model, we develop estimators inspired by Lorenz and Rossler’s principles to gauge the liquid level under specified liquid temperature, density, inlet velocity, and sensor placement conditions. The estimated results are compared with those of an artificial neural network (ANN) model. These ANN models learn and adapt to the patterns and features in data and catch non-linear relationships between input and output variables. The accuracy and error minimization of the developed model are confirmed through a thorough validation process. Experimental setups are employed to ensure the reliability and precision of the estimation results, thereby underscoring the robustness of our liquid-level measurement methodology. In summary, this study helps to estimate unmeasured states using the available measurements, which is essential for understanding and controlling the behavior of a system. It helps improve the performance and robustness of control systems, enhance fault detection capabilities, and contribute to dynamic systems’ overall efficiency and reliability

Design of an optimal observer for making liquid level control loop robust to variations in transmission parameters

The interconnected system has become the need of the hour in the present day industrial automation. Interconnection is established by conventional wired or by wireless techniques. In either case for the ease of data transmission discretization is essential as process variables are analog. This paper discusses the design of an observer to estimate the effects of Data Acquisition Cards (DAQ) like transmission delay and quantizer delay on a networked liquid level control loop. The objective of the proposed work is to accurately control the level of liquid in a tank, even if there are variations in the performance of the data acquisition card used to transmit the data in between the actual plant and computer. This can be achieved by designing an observer that will estimate the effect of data acquisition card parameters like transmission delay and quantizer delay on the system behavior of the process. A difference of observer output and existing process which is affected by the transmission parameter is filtered, so as eliminate the effect of variation in data acquisition parameters on the process. In the proposed work observer is designed using techniques like Luenberger and Kalman filter approach. Performance analysis shows that a Kalman filter-based observer produces better results as compared to a Luenberger observer. Results show that a Kalman filter-based observer produced a root mean square error of 0.015, and the root mean square of percentage overshoot of 1.16 for the test with the practical setup

Nanoparticle drug delivery systems in hepatocellular carcinoma: A focus on targeting strategies and therapeutic applications

Hepatocellular carcinoma (HCC) is recognized as a global health issue accounting for millions of deaths every year. Surgery, liver ablation, and embolization therapy are amongst the conventional methods for treatment of HCC. Chemotherapy plays a major role in HCC therapy, however, owing to its conventional pharmacotherapy limitations, it necessitates the development of novel therapeutic strategies. In contrast, nanomedicines for HCC have shown remarkable prospects for solving these complications in HCC owing to their high stability, controlled release, and high drug loading capacity. This review gives an insight into the nano-constructs used for HCC treatment and its active and passive targeting strategies. This review also inculcates the various approaches for targeting the liver cells, its targeting moieties and the conjugation chemistries involved in surface functionalization. A brief description of various therapeutic approaches in the treatment of HCC has also been discussed

SARS-CoV-2 seroprevalence among the general population and healthcare workers in India, December 2020–January 2021

Background: Earlier serosurveys in India revealed seroprevalence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) of 0.73% in May–June 2020 and 7.1% in August–September 2020. A third serosurvey was conducted between December 2020 and January 2021 to estimate the seroprevalence of SARS-CoV-2 infection among the general population and healthcare workers (HCWs) in India. Methods: The third serosurvey was conducted in the same 70 districts as the first and second serosurveys. For each district, at least 400 individuals aged ≥10 years from the general population and 100 HCWs from subdistrict-level health facilities were enrolled. Serum samples from the general population were tested for the presence of immunoglobulin G (IgG) antibodies against the nucleocapsid (N) and spike (S1-RBD) proteins of SARS-CoV-2, whereas serum samples from HCWs were tested for anti-S1-RBD. Weighted seroprevalence adjusted for assay characteristics was estimated. Results: Of the 28,598 serum samples from the general population, 4585 (16%) had IgG antibodies against the N protein, 6647 (23.2%) had IgG antibodies against the S1-RBD protein, and 7436 (26%) had IgG antibodies against either the N protein or the S1-RBD protein. Weighted and assay-characteristic-adjusted seroprevalence against either of the antibodies was 24.1% [95% confidence interval (CI) 23.0–25.3%]. Among 7385 HCWs, the seroprevalence of anti-S1-RBD IgG antibodies was 25.6% (95% CI 23.5–27.8%). Conclusions: Nearly one in four individuals aged ≥10 years from the general population as well as HCWs in India had been exposed to SARS-CoV-2 by December 2020