Implementation of a Cascade Fault Tolerant Control and Fault Diagnosis Design for a Modular Power Supply

The main objective of this research work was to develop reliable and intelligent power sources for the future. To achieve this objective, a modular stand-alone solar energy-based direct current (DC) power supply was designed and implemented. The converter topology used is a two-stage interleaved boost converter, which is monitored in closed loop. The diagnosis method is based on analytic redundancy relations (ARRs) deduced from the bond graph (BG) model, which can be used to detect the failures of power switches, sensors, and discrete components such as the output capacitor. The proposed supervision scheme including a passive fault-tolerant cascade proportional integral sliding mode control (PI-SMC) for the two-stage boost converter connected to a solar panel is suitable for real applications. Most model-based diagnosis approaches for power converters typically deal with open circuit and short circuit faults, but the proposed method offers the advantage of detecting the failures of other vital components. Practical experiments on a newly designed and constructed prototype, along with simulations under PSIM software, confirm the efficiency of the control scheme and the successful recovery of a faulty stage by manual isolation. In future work, the automation of this reconfiguration task could be based on the successful simulation results of the diagnosis method.This research was funded by the Tunisian Ministry of Higher Education and Scientific Research

Decentralized Federated Learning on the Edge over Wireless Mesh Networks

The rapid growth of Internet of Things (IoT) devices has generated vast amounts of data, leading to the emergence of federated learning as a novel distributed machine learning paradigm. Federated learning enables model training at the edge, leveraging the processing capacity of edge devices while preserving privacy and mitigating data transfer bottlenecks. However, the conventional centralized federated learning architecture suffers from a single point of failure and susceptibility to malicious attacks. In this study, we delve into an alternative approach called decentralized federated learning (DFL) conducted over a wireless mesh network as the communication backbone. We perform a comprehensive network performance analysis using stochastic geometry theory and physical interference models, offering fresh insights into the convergence analysis of DFL. Additionally, we conduct system simulations to assess the proposed decentralized architecture under various network parameters and different aggregator methods such as FedAvg, Krum and Median methods. Our model is trained on the widely recognized EMNIST dataset for benchmarking handwritten digit classification. To minimize the model's size at the edge and reduce communication overhead, we employ a cutting-edge compression technique based on genetic algorithms. Our simulation results reveal that the compressed decentralized architecture achieves performance comparable to the baseline centralized architecture and traditional DFL in terms of accuracy and average loss for our classification task. Moreover, it significantly reduces the size of shared models over the wireless channel by compressing participants' local model sizes to nearly half of their original size compared to the baselines, effectively reducing complexity and communication overhead

FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA

Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the raw local data. FL approaches have been adopted as a cornerstone of distributed machine learning (ML) to solve several complex use cases. FL presents an interesting interplay between communication and ML performance when implemented over distributed wireless nodes. Both the dynamics of networking and learning play an important role. In this article, we investigate the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks which employ CSMA/CA to schedule its transmissions. Our FL over CSMA/CA (FLCC) model is designed to eliminate untrusted devices and harness frequency reuse and spatial clustering techniques to improve the throughput required for coordinating a distributed implementation of FL in the wireless network. In our proposed model, frequency allocation is performed on the basis of spatial clustering performed using virtual cells. Each cell assigns a FL server and dedicated carrier frequencies to exchange the updated model's parameters within the cell. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss while considering the networking dynamics. We benchmark the proposed approach using a well-known MNIST dataset for performance evaluation. We demonstrate that the proposed approach outperforms the baseline FL algorithms in terms of explicitly defining the chosen users' criteria and achieving high accuracy in a robust network

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Intégration des réseaux bayésiens et bond graphs pour la supervision des systèmes dynamiques

The supervision of complex and critical industrial processes is a very heavy task which requires effective algorithms. The literature shows a growing interest of graphical approaches because of the simplicity of establishment of the derived algorithms. The model based diagnosis is a method which becomes widespread because of the richness of graphical and structural methods allowing modeling of most complex processes. The bond graph (BG) tool, with its multidisciplinary representation, is one of the most recognized approaches in this framework. In this context, we try in present work to couple this graphical approach with another graphical one allowing incorporating statistics of components failures. All this aims to mitigate the problems: unknown failure signatures or identical signatures for several components and monitoring the system degradation. Indeed, on the basis of consulted literature, it does not appear work which evokes a supervision strategy associating a Bayesian reliability model with a BG model based fault detection and isolation (FDI) approach. Consequently, the suggested work illustrates a method to outline this objective. We propose a new methodology for the supervision of the dynamic and hybrid dynamic systems. Our contribution appears in the proposal for a strategy of risk based supervision by combining two graphical approaches: BG and Bayesian networks (BN). The resulting model for diagnosis is a hybrid BN. It is able to make a decision under uncertainties of BG model and takes account of the probabilities of false alarm and non-detection. Furthermore, integration of two graphical approaches (BG and Bayesian networks (BN) to design robust supervision system is another innovative interest. Generated residuals from BG model are coupled with the component reliability model of components leading to a hybrid BN diagnostic model. This model is then used to make a decision under uncertainties of BG model and takes into account the probabilities of false alarm and non-detection. The developed theory is applied to a thermal power station.La supervision des processus industriels critiques est une tâche complexe qui nécessite des algorithmes robustes. La littérature montre un intérêt croissant des approches graphiques à cause de la simplicité de l’implémentation des algorithmes dérivés. Le diagnostic à base de modèle est une méthode qui devient de plus en plus utilisée à cause de la richesse des méthodes graphiques et structurelles permettant la modélisation des processus complexes et ne nécessitent pas une phase d’apprentissage en ligne. L’outil bond graph (BG) par ses propriétés graphique et multidisciplinaire est un outil puissant de modélisation reconnu. Dans ce contexte, les propriétés structurelles et causales de cet outil (utilisées pour la détection de défauts) sont exploitées ici pour intégrer les réseaux bayésiens graphiques permettant d’incorporer des statistiques de pannes des composants pour améliorer l’étape de décision. Cette méthodologie permet de pallier aux problèmes relatifs aux signatures de défauts inconnues ou identiques pour plusieurs composants et le suivie de la dégradation du système. Sur la base de la littérature consultée, il n’apparait pas de travaux qui évoquent une démarche pour la supervision associant un modèle bayésien de la fiabilité avec une approche de détection et isolation de défaut (FDI) basée sur le modèle BG. Notre contribution concerne l’intégration de deux outils graphiques (BG et réseaux Bayésiens (RB)) pour la conception d’un système de supervision robuste. Les résidus générés par le modèle BG sont couplés avec le modèle de fiabilité des composants à surveiller pour en déduire finalement un modèle de diagnostic de type RB hybride. Ce modèle est utilisé dans l’étape de décision face aux incertitudes du modèle bond graph en tenant compte des probabilités de fausses alarmes et de non détection estimées par une approche hiérarchique bayésienne. Une application à une partie d’une centrale thermique a validé la théorie développée

Intégration des réseaux bayésiens et bond graphs pour la supervision des systèmes dynamiques

The supervision of complex and critical industrial processes is a very heavy task which requires effective algorithms. The literature shows a growing interest of graphical approaches because of the simplicity of establishment of the derived algorithms. The model based diagnosis is a method which becomes widespread because of the richness of graphical and structural methods allowing modeling of most complex processes. The bond graph (BG) tool, with its multidisciplinary representation, is one of the most recognized approaches in this framework. In this context, we try in present work to couple this graphical approach with another graphical one allowing incorporating statistics of components failures. All this aims to mitigate the problems: unknown failure signatures or identical signatures for several components and monitoring the system degradation. Indeed, on the basis of consulted literature, it does not appear work which evokes a supervision strategy associating a Bayesian reliability model with a BG model based fault detection and isolation (FDI) approach. Consequently, the suggested work illustrates a method to outline this objective. We propose a new methodology for the supervision of the dynamic and hybrid dynamic systems. Our contribution appears in the proposal for a strategy of risk based supervision by combining two graphical approaches: BG and Bayesian networks (BN). The resulting model for diagnosis is a hybrid BN. It is able to make a decision under uncertainties of BG model and takes account of the probabilities of false alarm and non-detection. Furthermore, integration of two graphical approaches (BG and Bayesian networks (BN) to design robust supervision system is another innovative interest. Generated residuals from BG model are coupled with the component reliability model of components leading to a hybrid BN diagnostic model. This model is then used to make a decision under uncertainties of BG model and takes into account the probabilities of false alarm and non-detection. The developed theory is applied to a thermal power station.La supervision des processus industriels critiques est une tâche complexe qui nécessite des algorithmes robustes. La littérature montre un intérêt croissant des approches graphiques à cause de la simplicité de l’implémentation des algorithmes dérivés. Le diagnostic à base de modèle est une méthode qui devient de plus en plus utilisée à cause de la richesse des méthodes graphiques et structurelles permettant la modélisation des processus complexes et ne nécessitent pas une phase d’apprentissage en ligne. L’outil bond graph (BG) par ses propriétés graphique et multidisciplinaire est un outil puissant de modélisation reconnu. Dans ce contexte, les propriétés structurelles et causales de cet outil (utilisées pour la détection de défauts) sont exploitées ici pour intégrer les réseaux bayésiens graphiques permettant d’incorporer des statistiques de pannes des composants pour améliorer l’étape de décision. Cette méthodologie permet de pallier aux problèmes relatifs aux signatures de défauts inconnues ou identiques pour plusieurs composants et le suivie de la dégradation du système. Sur la base de la littérature consultée, il n’apparait pas de travaux qui évoquent une démarche pour la supervision associant un modèle bayésien de la fiabilité avec une approche de détection et isolation de défaut (FDI) basée sur le modèle BG. Notre contribution concerne l’intégration de deux outils graphiques (BG et réseaux Bayésiens (RB)) pour la conception d’un système de supervision robuste. Les résidus générés par le modèle BG sont couplés avec le modèle de fiabilité des composants à surveiller pour en déduire finalement un modèle de diagnostic de type RB hybride. Ce modèle est utilisé dans l’étape de décision face aux incertitudes du modèle bond graph en tenant compte des probabilités de fausses alarmes et de non détection estimées par une approche hiérarchique bayésienne. Une application à une partie d’une centrale thermique a validé la théorie développée

BAYESIAN RELIABILITY MODELS OF WEIBULL SYSTEMS: STATE OF THE ART

In the reliability modeling field, we sometimes encounter systems with uncertain structures, and the use of fault trees and reliability diagrams is not possible. To overcome this problem, Bayesian approaches offer a considerable efficiency in this context. This paper introduces recent contributions in the field of reliability modeling with the Bayesian network approach. Bayesian reliability models are applied to systems with Weibull distribution of failure. To achieve the formulation of the reliability model, Bayesian estimation of Weibull parameters and the model’s goodness-of-fit are evoked. The advantages of this modelling approach are presented in the case of systems with an unknown reliability structure, those with a common cause of failures and redundant ones. Finally, we raise the issue of the use of BNs in the fault diagnosis area

A Novel Two Stage Controller for a DC-DC Boost Converter to Harvest Maximum Energy from the PV Power Generation

In this article, an efficient and fast two-stage approach for controlling DC-DC boost converter using non linear sliding mode controller for a PV power plant is proposed. The control approach is based on two online methods instead of using the conventional combination of online and offline methods to harvest maximum energy and deliver an output PV voltage with reduced ripples. The proposed two-stage maximum power point tracking (MPPT) control can be integrated into many applications such as hybrid electric vehicles. Simulation results compared with the standard approaches P&O prove the tracking efficiency of the proposed method under fast changing atmospheric conditions of an average 99.87% and a reduced average ripple of 0.06. The two-stage MPPT control was implemented involving the embedded dSPACE DSP in comparison to the classical P&O to prove the efficiency and the validity of the control scheme. The experimental set-up system was carried out on boost converter and programmable DC electronic resistive load to highlights the robustness of the proposed controller against atmospheric changes and parametric variation. View Full-Tex

Decentralized Federated Learning on the Edge Over Wireless Mesh Networks

The rapid growth of Internet of Things (IoT) devices has generated vast amounts of data, leading to the emergence of federated learning as a novel distributed machine learning paradigm. Federated learning enables model training at the edge, leveraging the processing capacity of edge devices while preserving privacy and mitigating data transfer bottlenecks. However, the conventional centralized federated learning architecture suffers from a single point of failure and susceptibility to malicious attacks. In this study, we delve into an alternative approach called decentralized federated learning (DFL) conducted over a wireless mesh network as the communication backbone. We perform a comprehensive network performance analysis using stochastic geometry theory and physical interference models, offering fresh insights into the convergence analysis of DFL. Additionally, we conduct system simulations to assess the proposed decentralized architecture under various network parameters and different aggregator methods such as FedAvg, Krum and Median methods. Our model is trained on the widely recognized EMNIST dataset for benchmarking handwritten digit classification. To minimize the model’s size at the edge and reduce communication overhead, we employ a cutting-edge compression technique based on genetic algorithms. Our simulation results reveal that the compressed decentralized architecture achieves performance comparable to the baseline centralized architecture and traditional DFL in terms of accuracy and average loss for our classification task. Moreover, it significantly reduces the size of shared models over the wireless channel by compressing participants’ local model sizes to nearly half of their original size compared to the baselines, effectively reducing complexity and communication overhead