Search results

This study concerns tests carried out without lubrication on a universal lathe to study the influence of the magnetic field on the wear of a cutting tool made of metallic carbide. The tests parameters are: the intensity of the magnetic field (from 0 to 28.5 kA.m^-1), the cutting speed (from 6 to 136 m.min^-1), the advance speed and the cut depth (pass). Both of the previous parameters are fixed respectively at 0.125 mm.rev^-1 and 0.5 mm. The material being cut is made of XC38 steel. The tool wear is evaluated by weighing method (before and after each test). The results showed that the magnetic field has an influence on wear and that there is a critical value (H = 16.5 kA.m^-1) for which wear is minimum. Also, the scanning electron microscope (SEM) and optical microscope observations revealed the existence of three types of wear that are: flank wear, crater wear and wear by plastic deformation. Moreover, we concluded that, the increase of the magnetic field modifies the shape and the morphology of chips and rises the contact temperature.

The purpose of this paper is to propose a method to monitor a Wind Turbine’s (WT) main bearing, based on the difference between the temperature as measured by the Supervisory Control and Data Acquisition system (SCADA).

The monitoring of the main bearing is based on the difference between the measured temperature and the estimated temperature obtained from a dynamic model. The model used is based on the law of energy conservation. Several validation metrics have suggested that this model is accurate.

The Exponentially Weighted Moving Average control chart for two cases studies is used for the monitoring for the main bearing; this method has shown great potential for industrial applications. A failure was detected three weeks before the current actual alarm settings used by SCADA were able to identify the issue.

The proposed method is a monitoring method that can be used on most industrial wind farms and provide important information on the condition of the WTs’ main bearing.

The objective of this paper is to outline a framework that guides the development of sound maintenance strategies and policies for deep‐sea offshore wind turbines.

An important challenge with offshore wind energy production is to reduce the high operation and maintenance costs. To decrease complexity, and structure the maintenance strategy developing process, systems engineering principles are used.

The framework facilitates integration of fragmented but valuable information from different disciplines in the development of sound maintenance strategies. In addition, the framework may be used to identify knowledge gaps, and areas for further research.

The paper refers to research on deep‐sea offshore wind turbines, which is in its infancy, with a limited amount of data yet available for verification and validation. Deep‐sea offshore installations are not commercialized, and few pilot installations have been installed.

The design of the offshore wind turbines determines operation and maintenance features. Reducing operation and maintenance costs is necessary to make deep‐sea offshore wind projects viable in the first place. The framework contributes to the complicated development of maintenance strategies for a system not yet realized.

The purpose of this paper is to review the operation and maintenance practices within wind power applications and to clarify practical needs as gaps between researchers and practitioners.

The paper collects, categorizes, and analyzes the published literature of both researchers and practitioners systematically.

The paper defines significant issues in operation and maintenance of wind energy related to: site and seasonal asset disturbances; stakeholders’ requirements trade‐off; dependability and asset deterioration challenges; diagnostic, prognostic and information and communication technologies (ICTs) applications; and maintenance optimization models. Within each category, the gaps and further research needs have been extracted with respect to both an academic and industrial perspective.

The use of wind energy is growing rapidly and the associated practices related to maintenance and asset management are still lacking. Therefore, the literature review of operation and maintenance is a necessity to uncover the holistic issues and interrelationships of what has so far been published as detailed and fragmented topics to specific issues. Wind energy assets represent modern renewable energy assets which are affected by environmental disturbances, rapid technological development, rapid scaling‐up processes, the stochastic and dynamic nature of operations and degradation, the integrity and interoperability of system‐to‐support.

The paper provides a comprehensive review of research contributions and industrial development efforts. That will be useful to the life cycle stakeholders in both academia and industry in understanding the maintenance problem and solution space within the wind energy context.

Despite the wide dissemination and application of current signature analysis (CSA) in general industry, CSA is not commonly used in the wind industry, where the use of vibration signals predominates. Therefore, the purpose of this paper is to review the use of generator CSA (GCSA) in the online fault detection and diagnosis of wind turbines (WTs).

This is a bibliographical investigation in which the use of GCSA for the maintenance of WTs is analyzed. A section is dedicated to each of the main components, including the theoretical foundations on which GCSA is based and the methodology, mathematical models and signal processing techniques used by the proposals that exist on this topic.

The lack of appropriate technology and mathematical models, as well as the difficulty involved in performing actual studies in the field and the lack of research projects, has prevented the expansion of the use of GCSA for fault detection of other WT components. This research area has yet to be explored, and the existing investigations mainly focus on the gearbox and the doubly fed induction generator; however, modern signal treatment and artificial intelligence techniques could offer new opportunities in this field.

Although literature on the use of GCSA for the detection and diagnosis of faults in WTs has been published, these papers address specific applications for each of the WT components, especially gearboxes and generators. For this reason, the main contribution of this study is providing a comprehensive vision for the use of GCSA in the maintenance of WTs.

Globalization and the rapid development of Information and Communications Technology (ICT) have advantaged economies which have invested in (re)skilling their human capital in technical knowledge. Similarly, ICT have spearheaded the growth and development of industrial and service sectors in emerging economies. This accounts for the progress of some Asian economies and the lagging behind of many sub-Saharan African countries, including Ghana. Primarily, reorientation of economic development strategy and human development policy in tandem with the demands of knowledge-based economy (KBE) and related development in ICT explain the differences among the world's economies. Our chapter discusses the extent to which ICT has been incorporated into the educational system to transform it from traditional education in order to reskill human capital in postprimary schools to support the creation and growth of KBE in Ghana. Moreover, the chapter assesses whether ICT infrastructure and syllabuses at postprimary schools meet the challenges of a KBE and an enhanced growth development in Ghana. Using the theories of evolutionary economic change, new growth, technology and knowledge gap, we intend to analyze the progress and challenges faced by Ghana as it strives to build a KBE that thrives on innovation and creativity, which in turn drive economic growth and development. In this respect, we examine postprimary schools in Ghana.

An emerging application of Big Data is the addition of sensors and other micro‐electronic devices to engineer‐to‐order (ETO) goods such as one‐of‐a‐kind buildings and ships. The addition of micro‐electronic devices can enable the setting up and operation of smart buildings and smart ships. The purpose of this paper is to provide a critical realist analysis of Big Data hype. This is necessary to determine what challenges will need to be met before project businesses can achieve informational effects and transformational effects from Big Data technologies.

A critical realist study informed by reference to predictive theory and findings from action research. The predictive theory is concerned with the three different types of business effects that can come from information and communication technologies (ICTs): automational, informational, and transformational.

Critical realist analysis reveals that hype about Big Data underplays many challenges in achieving informational and transformational effects.

Many inter‐related non‐trivial factors need to be taken into account when considering investing in Big Data initiatives. These factors range from the planning of data sampling rates, through the robust fixing of sensors, to the implementation of data mining algorithms and signal models.

The originality of this paper is that critical realism is used in analysis of Big Data hype. The value of this paper is that it reveals a causal mechanism and causal context for project business Big Data application. This type of critical realist analysis can be applied to enable better understanding of necessary causal mechanisms and causal contexts for other ICT innovations.

The purpose of this study aims to focus on the detection and identification of the broken rotor bars (BRBs) of a squirrel cage induction motor (SCIM). The presented diagnosis technique is based on artificial neural networks (NNs) that use as inputs the results of the spectral analysis using the fast Fourier transform (FFT) of the reduced Park’s vector modulus (RPVM), along with the load values in which the motor operates.

First, this paper presents a comparative study between FFT applied on Hilbert modulus, Park’s vector modulus and RPVM to extract feature frequencies of BRB faults. Moreover, the extracted features of FFT applied to RPVM and the load values were selected as NNs’ inputs for the detection of the number of BRBs.

The obtained simulation results using MATLAB (Matrix Laboratory) environment show the effectiveness and accuracy of the proposed NNs based approach.

The current paper presents a novel diagnostic method for BRBs’ fault detection in SCIM, based on the combination between the signal processing analysis (FFT of RPVM) and artificial intelligence (NNs).

This paper aims to optimize the stiffness coefficient of the elastic element for a passive waist assistive exoskeleton (WAE). There is a tradeoff between stiffness coefficient of elastic element of the exoskeleton and work efficiency of the wearer, because elastic element affects original bending motion of the wearer and the force requirement of erector spinae is compensated by the other muscles. However, there is no accepted conclusion on how to determine the proper stiffness coefficient, especially with respected to the effort of groups of muscles, not only erector spinae.

In this study, a consumption indicator based on muscle fatigue of seven muscles is proposed and a Bayesian-based human-in-the-loop optimization approach is adopted to optimize the stiffness coefficient. Pneumatic artificial muscles are used to replace the mechanical elastic part to adjust the assistive force automatically. The proposed optimization method is verified by the way of load-lifting experiments with three different conditions: without exoskeleton, with fixed air pressure and with optimized air pressure. Six subjects participated in the experiment and each experiment is performed in different day.

Compared with No-Exo condition and static assistance condition, the parameter-optimized waist exoskeleton averagely reduces muscle fatigue of the six subjects by 45.30 ± 29.14% and 30.94 ± 30.29%, respectively. The experimental results indicate that the proposed method is effective to reduce muscle fatigue during stoop lifting task.

This paper provides a novel cost function construction method based on muscle fatigue and muscle synergy for passive WAE stiffness optimization.

This paper aims to deal with the design of new hybrid approach for the assistance of the flexion extension movement of the knee joint.

The control approach combines the use of a knee joint orthosis along with functional electrical stimulation (FES) within an assist-as-needed paradigm. An active impedance controller is used to assist the generation of muscular stimulation patterns during the extension sub-phase of the knee joint movement. The generated FES patterns are appropriately tailored to achieve flexion/extension movement of the knee joint, which allows providing the required assistance by the subject through muscular stimulation. The generated torque through stimulation is tracked by a non-linear disturbance observer and fed to the impedance controller to generate the desired trajectory that will be tracked using a standard proportional derivative controller.

The approach was tested in experiments with two healthy subjects. Results show satisfactory performances in terms of estimating the knee joint torque, as well as in terms of cooperation between the FES and the orthosis actuator during the execution of the knee joint flexion/extension movements.

The authors designed a new hybrid approach for the assistance of the flexion extension movement of the knee joint, which has not been studied yet. The control approach combines the use of a knee joint orthosis along with FES within an assist-as-needed paradigm.