Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

A Gaussian Mixture Model-Based Continuous Boundary Detection for 3D Sensor Networks

This paper proposes a high precision Gaussian Mixture Model-based novel Boundary Detection 3D (BD3D) scheme with reasonable implementation cost for 3D cases by selecting a minimum number of Boundary sensor Nodes (BNs) in continuous moving objects. It shows apparent advantages in that two classes of boundary and non-boundary sensor nodes can be efficiently classified using the model selection techniques for finite mixture models; furthermore, the set of sensor readings within each sensor node’s spatial neighbors is formulated using a Gaussian Mixture Model; different from DECOMO [1] and COBOM [2], we also formatted a BN Array with an additional own sensor reading to benefit selecting Event BNs (EBNs) and non-EBNs from the observations of BNs. In particular, we propose a Thick Section Model (TSM) to solve the problem of transition between 2D and 3D. It is verified by simulations that the BD3D 2D model outperforms DECOMO and COBOM in terms of average residual energy and the number of BNs selected, while the BD3D 3D model demonstrates sound performance even for sensor networks with low densities especially when the value of the sensor transmission range (r) is larger than the value of Section Thickness (d) in TSM. We have also rigorously proved its correctness for continuous geometric domains and full robustness for sensor networks over 3D terrains

Machine learning-based fault detection and diagnosis in electric motors

Fault diagnosis is critical to any maintenance industry, as early fault detection can prevent catastrophic failures as well as a waste of time and money. In view of these objectives, vibration analysis in the frequency domain is a mature technique. Although well established, traditional methods involve a high cost of time and people to identify failures, causing machine learning methods to grow in recent years. The Machine learning (ML) methods can be divided into two large learning groups: supervised and unsupervised, with the main difference between them being whether the dataset is labeled or not. This study presents a total of four different methods for fault detection and diagnosis. The frequency analysis of the vibration signal was the first approach employed. This analysis was chosen to validate the future results of the ML methods. The Gaussian Mixture model (GMM) was employed for the unsupervised technique. A GMM is a probabilistic model in which all data points are assumed to be generated by a finite number of Gaussian distributions with unknown parameters. For supervised learning, the Convolution neural network (CNN) was used. CNNs are feedforward networks that were inspired by biological pattern recognition processes. All methods were tested through a series of experiments with real electric motors. Results showed that all methods can detect and classify the motors in several induced operation conditions: healthy, unbalanced, mechanical looseness, misalignment, bent shaft, broken bar, and bearing fault condition. Although all approaches are able to identify the fault, each technique has benefits and limitations that make them better for certain types of applications, therefore, a comparison is also made between the methods.O diagnóstico de falhas é fundamental para qualquer indústria de manutenção, a detecção precoce de falhas pode evitar falhas catastróficas, bem como perda de tempo e dinheiro. Tendo em vista esses objetivos, a análise de vibração através do domínio da frequência é uma técnica madura. Embora bem estabelecidos, os métodos tradicionais envolvem um alto custo de tempo e pessoas para identificar falhas, fazendo com que os métodos de aprendizado de máquina cresçam nos últimos anos. Os métodos de Machine learning (ML) podem ser divididos em dois grandes grupos de aprendizagem: supervisionado e não supervisionado, sendo a principal diferença entre eles é o conjunto de dados que está rotulado ou não. Este estudo apresenta um total de quatro métodos diferentes para detecção e diagnóstico de falhas. A análise da frequência do sinal de vibração foi a primeira abordagem empregada. foi escolhida para validar os resultados futuros dos métodos de ML. O Gaussian Mixture Model (GMM) foi empregado para a técnica não supervisionada. O GMM é um modelo probabilístico em que todos os pontos de dados são considerados gerados por um número finito de distribuições gaussianas com parâmetros desconhecidos. Para a aprendizagem supervisionada, foi utilizada a Convolutional Neural Network (CNN). CNNs são redes feedforward que foram inspiradas por processos de reconhecimento de padrões biológicos. Todos os métodos foram testados por meio de uma série de experimentos com motores elétricos reais. Os resultados mostraram que todos os métodos podem detectar e classificar os motores em várias condições de operação induzida: íntegra, desequilibrado, folga mecânica, desalinhamento, eixo empenado, barra quebrada e condição de falha do rolamento. Embora todas as abordagens sejam capazes de identificar a falha, cada técnica tem benefícios e limitações que as tornam melhores para certos tipos de aplicações, por isso, também e feita uma comparação entre os métodos

Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle

The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian Mixture Model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on Artificial Neural Networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios

Analytical Report on Metaheuristic and Non-Metaheuristic Algorithms for Clustering in Wireless Networks

This analytical report delves into the comprehensive evaluation of both metaheuristic and non-metaheuristic algorithms utilized for clustering in wireless networks. Clustering techniques play a pivotal role in enhancing the efficiency and performance of wireless networks by organizing nodes into meaningful groups. Metaheuristic algorithms, inspired by natural processes, offer innovative solutions to complex optimization problems, while non-metaheuristic algorithms rely on traditional mathematical principles. This report systematically compares and contrasts the efficacy of various algorithms, considering key metrics such as convergence speed, scalability, robustness, and adaptability to dynamic network conditions. By scrutinizing both categories of algorithms, this report aims to provide a holistic understanding of their respective advantages, limitations, and applicability in wireless network clustering scenarios. The insights derived from this analysis can guide network engineers, researchers, and practitioners in selecting the most suitable algorithms based on specific network requirements, ultimately contributing to the advancement of wireless network clustering techniques

Research of Applying Machine Learning Methods to Outlier Detection in Wireless Sensor Networks

兵庫県立大学大学院201

Prognostic Algorithms for Condition Monitoring and Remaining Useful Life Estimation

To enable the benets of a truly condition-based maintenance philosophy to be realised, robust, accurate and reliable algorithms, which provide maintenance personnel with the necessary information to make informed maintenance decisions, will be key. This thesis focuses on the development of such algorithms, with a focus on semiconductor manufacturing and wind turbines. An introduction to condition-based maintenance is presented which reviews dierent types of maintenance philosophies and describes the potential benets which a condition- based maintenance philosophy will deliver to operators of critical plant and machinery. The issues and challenges involved in developing condition-based maintenance solutions are discussed and a review of previous approaches and techniques in fault diagnostics and prognostics is presented. The development of a condition monitoring system for dry vacuum pumps used in semi- conductor manufacturing is presented. A notable feature is that upstream process mea- surements from the wafer processing chamber were incorporated in the development of a solution. In general, semiconductor manufacturers do not make such information avail- able and this study identies the benets of information sharing in the development of condition monitoring solutions, within the semiconductor manufacturing domain. The developed solution provides maintenance personnel with the ability to identify, quantify, track and predict the remaining useful life of pumps suering from degradation caused by pumping large volumes of corrosive uorine gas. A comprehensive condition monitoring solution for thermal abatement systems is also presented. As part of this work, a multiple model particle ltering algorithm for prog- nostics is developed and tested. The capabilities of the proposed prognostic solution for addressing the uncertainty challenges in predicting the remaining useful life of abatement systems, subject to uncertain future operating loads and conditions, is demonstrated. Finally, a condition monitoring algorithm for the main bearing on large utility scale wind turbines is developed. The developed solution exploits data collected by onboard supervisory control and data acquisition (SCADA) systems in wind turbines. As a result, the developed solution can be integrated into existing monitoring systems, at no additional cost. The potential for the application of multiple model particle ltering algorithm to wind turbine prognostics is also demonstrated

Particle Filters for Remaining Useful Life Estimation of Abatement Equipment used in Semiconductor Manufacturing

Prognostics is the ability to predict the remaining useful life of a specific system, or component, and represents a key enabler of any effective condition-based-maintenance strategy. Among methods for performing prognostics such as regression and artificial neural networks, particle filters are emerging as a technique with considerable potential. Particle filters employ both a state dynamic model and a measurement model, which are used together to predict the evolution of the state probability distribution function. The approach has similarities to Kalman filtering, however, particle filters make no assumptions that the state dynamic model be linear or that Gaussian noise assumptions must hold true. The technique is applied in predicting the degradation of thermal processing units used in the treatment of waste gases from semiconductor processing chambers. The performance of the technique demonstrates the potential of particle filters as a robust method for accurately predicting system failure. In addition to the use of particle filters, Gaussian Mixture Models (GMM) are employed to extract signals associated with the different operating modes from a multi-modal signal generated by the operating characteristics of the thermal processing unit

Fault Detection and Diagnosis Encyclopedia for Building Systems:A Systematic Review

This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository