The k-means algorithm: A comprehensive survey and performance evaluation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine

Data-driven model-based approaches to condition monitoring and improving power output of wind turbines

The development of the wind farm has grown dramatically in worldwide over the past 20 years. In order to satisfy the reliability requirement of the power grid, the wind farm should generate sufficient active power to make the frequency stable. Consequently, many methods have been proposed to achieve optimizing wind farm active power dispatch strategy. In previous research, it assumed that each wind turbine has the same health condition in the wind farm, hence the power dispatch for healthy and sub-healthy wind turbines are treated equally. It will accelerate the sub-healthy wind turbines damage, which may leads to decrease generating efficiency and increases operating cost of the wind farm. Thus, a novel wind farm active power dispatch strategy considering the health condition of wind turbines and wind turbine health condition estimation method are the proposed. A modelbased CM approach for wind turbines based on the extreme learning machine (ELM) algorithm and analytic hierarchy process (AHP) are used to estimate health condition of the wind turbine. Essentially, the aim of the proposed method is to make the healthy wind turbines generate power as much as possible and reduce fatigue loads on the sub-healthy wind turbines. Compared with previous methods, the proposed methods is able to dramatically reduce the fatigue loads on subhealthy wind turbines under the condition of satisfying network operator active power demand and maximize the operation efficiency of those healthy turbines. Subsequently, shunt active power filters (SAPFs) are used to improve power quality of the grid by mitigating harmonics injected from nonlinear loads, which is further to increase the reliability of the wind turbine system

Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

As an environmentally friendly transport option, electric vehicles (EVs) are endowed with the characteristics of low fossil energy consumption and low pollutant emissions. In today's growing market share of EVs, the safety and reliability of the powertrain system will be directly related to the safety of human life. Reliability problems of EV powertrains may occur in any power electronic (PE) component and mechanical part, both sudden and cumulative. These faults in different locations and degrees will continuously threaten the life of drivers and pedestrians, bringing irreparable consequences. Therefore, monitoring and predicting the real-time health status of EV powertrain is a high-priority, arduous and challenging task. The purposes of this study are to develop AI-supported effective safety improvement techniques for EV powertrains. In the first place, a literature review is carried out to illustrate the up-to-date AI applications for solving condition monitoring and fault detection issues of EV powertrains, where recent case studies between conventional methods and AI-based methods in EV applications are compared and analysed. On this ground this study, then, focuses on the theories and techniques concerning this topic so as to tackle different challenges encountered in the actual applications. In detail, first, as for diagnosing the bearing system in the earlier fault period, a novel inferable deep distilled attention network is designed to detect multiple bearing faults. Second, a deep learning and simulation driven approach that combines the domain-adversarial neural network and the lumped-parameter thermal network (LPTN) is proposed for achieve IPMSM permanent magnet temperature estimation work. Finally, to ensure the use safety of the IGBT module, deep learning -based IGBT modules’ double pulse test (DPT) efficiency enhancement is proposed and achieved via multimodal fusion networks and graph convolution networks

On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator

Abstract The reliable performance of power electronic modules has been a concern for many years due to their increased use in applications which demand high availability and longer lifetimes. Thick Al wire bonding is a key technique for providing interconnections in power electronic modules. Today, wire bond lift-off and heel cracking are often considered the most lifetime limiting factors of power electronic modules as a result of cyclic thermomechanical stresses. Therefore, it is important for power electronic packaging manufacturers to address this issue at the design stage and on the manufacturing line. Techniques for the non-destructive, real-time evaluation and control of wire bond quality have been proposed to detect defects in manufacture and predict reliability prior to in-service exposure. This approach has the potential to improve the accuracy of lifetime prediction for the manufactured product. In this thesis, a non-destructive technique for detecting bond quality by the application of a semi-supervised classification algorithm to process signals obtained from an ultrasonic generator is presented. Experimental tests verified that the classification method is capable of accurately predicting bond quality, indicated by bonded area as measured by X-ray tomography. Samples classified during bonding were subjected to both passive and active cycling and the distribution of bond life amongst the different classes analysed. It is demonstrated that the as-bonded quality classification is closely correlated with cycling life and can therefore be used as a non-destructive tool for monitoring bond quality and predicting useful service life

On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator

Abstract The reliable performance of power electronic modules has been a concern for many years due to their increased use in applications which demand high availability and longer lifetimes. Thick Al wire bonding is a key technique for providing interconnections in power electronic modules. Today, wire bond lift-off and heel cracking are often considered the most lifetime limiting factors of power electronic modules as a result of cyclic thermomechanical stresses. Therefore, it is important for power electronic packaging manufacturers to address this issue at the design stage and on the manufacturing line. Techniques for the non-destructive, real-time evaluation and control of wire bond quality have been proposed to detect defects in manufacture and predict reliability prior to in-service exposure. This approach has the potential to improve the accuracy of lifetime prediction for the manufactured product. In this thesis, a non-destructive technique for detecting bond quality by the application of a semi-supervised classification algorithm to process signals obtained from an ultrasonic generator is presented. Experimental tests verified that the classification method is capable of accurately predicting bond quality, indicated by bonded area as measured by X-ray tomography. Samples classified during bonding were subjected to both passive and active cycling and the distribution of bond life amongst the different classes analysed. It is demonstrated that the as-bonded quality classification is closely correlated with cycling life and can therefore be used as a non-destructive tool for monitoring bond quality and predicting useful service life

Measurements, Models, Systems and Design

531 s.

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section