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Efficient gearbox fault diagnosis is crucial for the cost-effective maintenance and reliable operation of rotating machinery. Despite extensive research, effective fault diagnosis remains challenging due to the multitude of features available for classification. Traditional feature selection methods often fail to achieve optimal performance in fault classification tasks. This study introduces diverse ranking methods for selecting the relevant features and utilizes data segmentation techniques such as sliding, windowing, and bootstrapping to strengthen predictive model performance and scalability. A comparative analysis of these methods was conducted to identify the potential causes and future solutions. An evaluation of the impact of enhanced feature engineering and data segmentation on predictive maintenance in gearboxes revealed promising outcomes, with decision trees, SVM, and KNN models outperforming others. Additionally, within a fully connected network, windowing emerged as a more robust and efficient segmentation method compared to bootstrapping. Further research is necessary to assess the performance of these techniques across diverse datasets and applications, offering comprehensive insights for future studies in fault diagnosis and predictive maintenance.

Prior to the year 2000, air conditioning was not common in many cities throughout the world. However, today, 20 years later, air conditioning is common. This circumstance has a negative impact on the climate. Additionally, the situation regarding energy usage as a result of this is alarming. For a healthy and pleasant livelihood, indoor temperature and air flow must be controlled. False partitions with insulating layers have been used to regulate the temperature inside rooms, but they are unable to regulate the variation in humidity caused by the exchange of water between interior and exterior walls. In this manuscript, we provide a sensory system that can automatically detect relative humidity and temperature. Temperature is sensed at each layer of the false partition using an LM35-based integrated circuit and humidity is detected by an FA–ZnO nano-composite layer through an indoor false partition owing to changes in the material’s resistance. Depending upon the change in resistance based on the fluctuation in temperature, the corresponding current responds by arduino microcontroller, and thus triggers the automated ON and OFF switch for air conditioning. Living and non-living bodies both lead pleasant and healthy lives when indoor units are managed properly.

Electroactive Polymer (EAP) hydrogels are an active matter material used as actuators in soft robotics. Hydrogels exhibit active matter behavior through a form of memory and can be used to embody memory systems such as automata. This study exploited EAP responses, finding that EAP memory functions could be utilized for automaton and reservoir computing frameworks. Under sequential electrical stimulation, the mechanical responses of EAPs were represented in a probabilistic Moore automaton framework and expanded through shaping the reservoir's energy landscape. The EAP automaton reservoir's computational ability was compared with digital computation to assess EAPs as computational resources. We found that the computation in the EAP's reaction to stimuli can be presented through automaton structures, revealing a potential bridge between EAP's use as an integrated actuator and controller, i.e., our automaton framework could potentially lead to control systems wherein the computation was embedded into the media dynamical responses.

The paper presents a bespoke power measurement system that has been designed and implemented on a Rubber Tyre Gantry, RTG crane at the Port of Felixstowe, PoF. The design of a novel system topology which is centred around a Beckhoff computer, as well as the specifications of the various transducers used, is detailed. To complement the hardware installation, a bespoke data analysis tool has also been developed in MATLAB, an overview of its operation and use are presented. The system allows for the characterisation of the cranes’ various motors during operational hours; furthermore, the data collected has been used to verify simulations of RTGs developed as part of the overall PoF project. The measurement system was installed on RTG1 at the port in March 2016.

Given the increase in international trading and the significant energy and environmental challenges in ports around the world, there is a need for a greater understanding of the energy demand behaviour at ports. The move towards electrified rubber-tyred gantry (RTG) cranes is expected to reduce gas emissions and increase energy savings compared to diesel RTG cranes but it will increase electrical energy demand. Electrical load forecasting is a key tool for understanding the energy demand which is usually applied to data with strong regularities and seasonal patterns. However, the highly volatile and stochastic behaviour of the RTG crane demand creates a substantial prediction challenge. This paper is one of the first extensive investigations into short term load forecasts for electrified RTG crane demand. Options for model inputs are investigated depending on extensive data and correlation analysis. The effect of estimation accuracy of exogenous variables on the forecast accuracy is investigated as well. The models are tested on two different RTG crane data sets that were collected from the Port of Felixstowe in the UK. The results reveal the effectiveness of the forecast models when the estimation of the number of crane moves and container gross weight are accurate.

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market.

The purpose of this paper is to study and analyse the energy that is used by the various motors of a craneof the Rubber Tyred Gantry type. For this reason a single Rubber Tyred Gantry (RTG) crane has beeninstrumented at port of Felixstowe and data has been collected during normal operation for eight days.This data has been analysed in terms of active and idle modes and also in terms of energy usage by thevarious motors. From this analysis it is possible to determine that on average about half of the energyconsumed is potentially recoverable. It is also estimated that the recovery of this proportion of energycould lead to savings of 32,600 L of fuel and 8100 tonnes of CO2per year at Port of Felixstowe.

Transmission line impedance parameter estimation forms an essential part in modeling and monitoring of electricity networks. The accuracy of the estimated parameters has a direct impact on various functions of network operation, such as state estimation, fault location, and dynamic thermal line rating. Impedance parameters can be estimated from voltage and current measurements taken at the ends of the transmission line. Previous research has produced a range of methods with the purpose of maximizing the accuracy of the estimated values. However, the input measurements from the line ends can contain systematic errors that are introduced by the instrumentation channel and significantly reduce impedance parameter accuracy, which is not taken into account by most of the existing methods. In this paper, a novel method is presented that estimates correction factors for the systematic errors and, thus, increases the accuracy of impedance parameter values. The performance of the new method is compared with an existing one in a case study on laboratory measurements.

© 2017, Electromagnetics Academy. All rights reserved. This paper presents a general theoretical analysis of the Wireless Power Transfer (WPT) efficiency that exists between electrically short, Perfect Electric Conductor (PEC) electric and magnetic dipoles, with particular relevance to near-field applications. The figure of merit for the dipoles is derived in closed-form, and used to study the WPT efficiency as the criteria of interest. The analysis reveals novel results regarding the WPT efficiency for both sets of dipoles, and describes how electrically short perfectly conducting dipoles can achieve efficient WPT over distances that are considerably greater than their size.

© 2017 The Institution of Engineering and Technology. All rights reserved. Change in consumer behaviour through uptake of low carbon technologies is likely to put existing distribution networks under strain and worsen the operational requirements of the network. Deployment of energy storage and power electronics is a feasible alternative to traditional network reinforcement. This study presents two control algorithms used with an energy storage device deployed as part of New Thames Valley Vision Project. The two algorithms are aimed at (i) equalising phase loading with correction of power factor and (ii) providing voltage support with Additive Increase Multiplicative Decrease algorithm for active and reactive power control.

© 2017 The Institution of Engineering and Technology. All rights reserved. Uptake of low carbon technologies could likely lead to increased demand in distribution networks and consequently could impose additional stress on the networks. Battery energy storage systems (BESS) are identified as a feasible alternative to traditional network reinforcement. This study analyses two BESS scheduling algorithms (model predictive control (MPC) and fixed schedule) supplied with forecasts from five methods for predicting demand on 100 low-voltage feeders. Results show that forecasting feeders with higher mean daily demand produce lower mean absolute errors and better peak demand reduction. MPC with simple error improves peak reduction over fixed schedule for feeders with lower mean daily demand.

© 2013 IEEE. This paper tackles the problem of the estimation of simplified human limb kinematics for home health care. Angular kinematics are widely used for gait analysis, for rehabilitation, and more generally for activity recognition. Residential monitoring requires particular sensor constraints to enable long-term user compliance. The proposed strategy is based on measurements from two low-power accelerometers placed only on the forearm, which makes it an ill-posed problem. The system is considered in a Bayesian framework, with a linear-Gaussian transition model with hard boundaries and a nonlinear-Gaussian observation model. The state vector and the associated covariance are estimated by a post-regularized particle filter (constrained-extended-RPF or C-ERPF), with an importance function whose moments are computed via an extended Kalman filter (EKF) linearization. Several sensor configurations are compared in terms of estimation performance, as well as power consumption and user acceptance. The proposed constrained-EKF (CERPF) is compared to other methods (EKF, constrained-EKF, and ERPF without transition constraints) on the basis of simulations and experimental measurements with motion capture reference. The proposed C-ERPF method coupled with two accelerometers on the wrist provides promising results with 19% error in average on both angles, compared with the motion capture reference, 10% on velocities and 7% on accelerations. This comparison highlights that arm kinematics can be estimated from only two accelerometers on the wrist. Such a system is a crucial step toward enabling machine monitoring of users health and activity on a daily basis.

© 2017 by the authors. This article presents a study of optimal control strategies for an energy storage system connected to a network of electrified Rubber Tyre Gantry (RTG) cranes. The study aims to design optimal control strategies for the power flows associated with the energy storage device, considering the highly volatile nature of RTG crane demand and difficulties in prediction. Deterministic optimal energy management controller and a Model Predictive Controller (MPC) are proposed as potentially suitable approaches to minimise the electric energy costs associated with the real-time electricity price and maximise the peak demand reduction, under given energy storage system parameters and network specifications. A specific case study is presented in to test the proposed optimal strategies and compares them to a set-point controller. The proposed models used in the study are validated using data collected from an instrumented RTG crane at the Port of Felixstowe, UK and are compared to a standard set-point controller. The results of the proposed control strategies show a significant reduction in the potential electricity costs and peak power demand from the RTG cranes.

There is a growing interest in measuring human activities via worn inertial sensors, and situating two accelerometers on a body segment allows accessing rotational kinematic information, at a significantly lower energy cost when compared with gyroscopes. However, the placement of sensors has not been widely considered in the literature. In practice, dual-accelerometer systems should be built as compact as possible to ensure long-term wearability. In this paper, the impact of sensor placement and nature of human activity on signal quality is quantified by individual and differential signal-to-noise ratios (SNRs). To do so, noise-free signals are described by a 2-D kinematic model of a body segment as a function of kinematic variables and sensor location on the segment. Measurements are modelled as kinematic signals disturbed by zero mean additive Gaussian noise. Depending on the accuracy needed, one can choose a minimal SNR to achieve, with such dual-accelerometer arrangement. We estimate SNR and minimal sensor separations for three data sets, two from the public domain and one collected for this paper. The data sets give arm motion profiles for reaching, inertial data collected during locomotion on a treadmill and during activities of daily life. With a dual-accelerometer arrangement, we show that it is possible to achieve a good differential SNR for the analysis of various human activities if the separation between the two sensors and their placement is well chosen.

In this paper, a power management strategy (PMS) has been developed for the control of energy storage in a system subjected to loads of random duration. The PMS minimises the costs associated with the energy consumption of specific systems powered by a primary energy source and equipped with energy storage, under the assumption that the statistical distribution of load durations is known. By including the variability of the load in the cost function, it was possible to define the optimality criteria for the power flow of the storage. Numerical calculations have been performed obtaining the control strategies associated with the global minimum in energy costs, for a wide range of initial conditions of the system. The results of the calculations have been tested on a MATLAB/Simulink model of a rubber tyre gantry (RTG) crane equipped with a flywheel energy storage system (FESS) and subjected to a test cycle, which corresponds to the real operation of a crane in the Port of Felixstowe. The results of the model show increased energy savings and reduced peak power demand with respect to existing control strategies, indicating considerable potential savings for port operators in terms of energy and maintenance costs.

Real-time estimation of power transmission line impedance parameters has become possible with the availability of synchronized phasor (synchrophasor) measurements of voltage and current. If sufficiently accurate, the estimated parameter values are a powerful tool for improving the performance of a range of power system monitoring, protection, and control applications, including fault location and dynamic thermal line rating. The accuracy of the parameter estimates can be reduced by unknown errors in the synchrophasors that are introduced in the measurement process. In this paper, a method is proposed with the aim of obtaining accurate estimates of potentially variable impedance parameters, in the presence of systematic errors in voltage and current measurements. The method is based on optimization to identify correction constants for the phasors. A case study of a simulated transmission line is presented to demonstrate the effectiveness of the new method, which is better in comparison with a previously proposed method. The results, as well as limits, and the potential extensions of the new method are discussed.

A method for calculating the power demand of the hoist motor in rubber-tired gantry (RTG) cranes with nonparallel cables has been developed to measure the energy consumption in a typical lift cycle. From measurements taken at the Port of Felixstowe, it has been observed that the peculiar geometrical characteristics of the hoisting mechanism cause the power demand to increase with the container height in constant rotor speed conditions. The change in the angle of the hoisting ropes causes an increase in torque load and power consumption. By using information extracted from the crane's geometry, it has been possible to calculate the potential energy increase given the weight and vertical position of the container. The load torque on the hoist motor and the vertical speed of the mass have also been calculated, allowing for the modeling of the hoist motor power consumption when lifting containers with constant rotational speed. The proposed model has been compared to a constant power demand approximation, showing a higher accuracy for masses below 40 t.

FES assisted activities such as standing, walking, cycling and rowing induce forces within the leg bones and have been proposed to reduce osteoporosis in spinal cord injury (SCI). However, details of the applied mechanical stimulus for osteogenesis is often not reported. Typically, comparisons of bone density results are made after costly and time consuming clinical trials. These studies have produced inconsistent results and are subject to sample size variations. Here we propose a design process that may be used to predict the clinical outcome based on biomechanical simulation and mechano-biology. This method may allow candidate therapies to be optimized and quantitatively compared. To illustrate the approach we have used data obtained from a rower with complete paraplegia using the RowStim (III) system.

The efficiency of a Wireless Power Transfer (WPT) system is greatly dependent on both the geometry and operating frequency of the transmitting and receiving structures. By using Coupled Mode Theory (CMT), the figure of merit is calculated for resonantly-coupled loop and dipole systems. An in-depth analysis of the figure of merit is performed with respect to the key geometric parameters of the loops and dipoles, along with the resonant frequency, in order to identify the key relationships leading to high-efficiency WPT. For systems consisting of two identical single-turn loops, it is shown that the choice of both the loop radius and resonant frequency are essential in achieving high-efficiency WPT. For the dipole geometries studied, it is shown that the choice of length is largely irrelevant and that as a result of their capacitive nature, low-MHz frequency dipoles are able to produce significantly higher figures of merit than those of the loops considered. The results of the figure of merit analysis are used to propose and subsequently compare two mid-range loop and dipole WPT systems of equal size and operating frequency, where it is shown that the dipole system is able to achieve higher efficiencies than the loop system of the distance range examined.

This paper assesses the impact of the location and configuration of Battery Energy Storage Systems (BESS) on Low-Voltage (LV) feeders. BESS are now being deployed on LV networks by Distribution Network Operators (DNOs) as an alternative to conventional reinforcement (e.g. upgrading cables and transformers) in response to increased electricity demand from new technologies such as electric vehicles. By storing energy during periods of low demand and then releasing that energy at times of high demand, the peak demand of a given LV substation on the grid can be reduced therefore mitigating or at least delaying the need for replacement and upgrade. However, existing research into this application of BESS tends to evaluate the aggregated impact of such systems at the substation level and does not systematically consider the impact of the location and configuration of BESS on the voltage profiles, losses and utilisation within a given feeder. In this paper, four configurations of BESS are considered: single-phase, unlinked three-phase, linked three-phase without storage for phase-balancing only, and linked three-phase with storage. These four configurations are then assessed based on models of two real LV networks. In each case, the impact of the BESS is systematically evaluated at every node in the LV network using Matlab linked with OpenDSS. The location and configuration of a BESS is shown to be critical when seeking the best overall network impact or when considering specific impacts on voltage, losses, or utilisation separately. Furthermore, the paper also demonstrates that phase-balancing without energy storage can provide much of the gains on unbalanced networks compared to systems with energy storage.