Rule-based classification approach for railway wagon health monitoring

Modern machine learning techniques have encouraged interest in the development of vehicle health monitoring systems that ensure secure and reliable operations of rail vehicles. In an earlier study, an energy-efficient data acquisition method was investigated to develop a monitoring system for railway applications using modern machine learning techniques, more specific classification algorithms. A suitable classifier was proposed for railway monitoring based on relative weighted performance metrics. To improve the performance of the existing approach, a rule-based learning method using statistical analysis has been proposed in this paper to select a unique classifier for the same application. This selected algorithm works more efficiently and improves the overall performance of the railway monitoring systems. This study has been conducted using six classifiers, namely REPTree, J48, Decision Stump, IBK, PART and OneR, with twenty-five datasets. The Waikato Environment for Knowledge Analysis (WEKA) learning tool has been used in this study to develop the prediction models

Predicting vertical acceleration of railway wagons using regression algorithms

The performance of rail vehicles running on railway tracks is governed by the dynamic behaviors of railway bogies, particularly in cases of lateral instability and track irregularities. To ensure reliable, safe, and secure operation of railway systems, it is desirable to adopt intelligent monitoring systems for railway wagons. In this paper, a forecasting model is developed to investigate the vertical-acceleration behavior of railway wagons that are attached to a moving locomotive using modern machine-learning techniques. Both front- and rear-body vertical-acceleration conditions are predicted using popular regression algorithms. Different types of models can be built using a uniform platform to evaluate their performance. The estimation techniques' performance has been measured using a set of attributes' correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE), and computational complexity for each of the algorithms. Statistical hypothesis analysis is applied to determine the most suitable regression algorithm for this application. Finally, spectral analysis of the front- and rear-body vertical condition is produced from the predicted data using the fast Fourier transform (FFT) and is used to generate precautionary signals and system status that can be used by a locomotive driver for necessary actions

Application of machine learning techniques for railway health monitoring

Emerging wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems that ensure secure and reliable operation of the rail vehicle. The performance of rail vehicles running on railway tracks is governed by the dynamic behaviours of railway bogies especially in the cases of lateral instability and track irregularities. In order to ensure safety and reliability of railway in this chapter, a forecasting model has been developed to investigate vertical acceleration behaviour of railway wagons attached to a moving locomotive using modern machine learning techniques. Initially, an energy-efficient data acquisition model has been proposed for WSN applications using popular learning algorithms. Later, a prediction model has been developed to investigate both front and rear body vertical acceleration behaviour. Different types of models can be built using a uniform platform to evaluate their performances and estimate different attributes’ correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE) and computation complexity for each of the algorithm. Finally, spectral analysis of front and rear body vertical condition is produced from the predicted data using Fast Fourier Transform (FFT) and used to generate precautionary signals and system status which can be used by the locomotive driver for deciding upon necessary actions

Economic analysis of hybrid renewable model for subtropical climate

Current power systems create environmental impacts due to utilization of fossil fuels, especially coal, as carbon dioxide is emitted into the atmosphere. In contrast to fossil fuels, renewable energy offers alternative sources of energy which are in general pollution free, technologically effective and environmentally sustainable. There is an increased interest in renewable energy, particularly solar and wind energy, which provides electricity without giving rise to carbon dioxide emissions. This paper presents economic analysis of a renewable hybrid system for a subtropical climate and also investigated the impact of renewable energy sources to the existing and future smart power system. The daily mean global solar irradiance and three hourly mean wind speed have been collected from the Rockhampton Aero Weather Station, Queensland (RAWS), Australia for this study. Hybrid Optimization Model for Electric Renewable (HOMER), a computer model developed by National Renewable Energy Laboratory (NREL) has been used to perform comparative analysis of solar and wind energy with diesel and hybrid systems. Initially total net present cost (NPC), cost of energy (COE) and the renewable fraction (RF) have been measured as performances metrics to compare the performances of different systems. For better optimization, the model has been refined with sensitivity analysis which explores performance variations due to wind speed, solar irradiation and diesel fuel prices. From the simulation, it is shown that there are a number of factors that impact the integration and performance of renewable energy sources to the power systems

Potential challenges : integrating renewable energy with the smart grid

This is the published version

Prospects of solar energy in Australia

Today, more than 80% of energy is produced from fossil fuels that pollute the air and surrounding environments each and every day, creating global warming. Therefore it is time to think about alternative sources of energy to build a climate friendly environment. In contrast to fossil fuels, renewable energy offers alternative sources of energy which are in general pollution free, unlimited, and environmentally sustainable. This paper presents a feasibility study undertaken to investigate the prospects of solar energy for the climate similar to Australia so as to further investigate the impacts of renewable energy sources in existing and future smart power systems. The monthly average global solar radiation has been collected for twenty-one locations in Australia from the National Aeronautics and Space Administration (NASA). Hybrid Optimisation Model for Electric Renewable (HOMER), and Renewable-energy and Energy-efficient Technologies (RETScreen) computer tools were used to perform comparative analysis of solar energy with diesel and hybrid systems. Initially, total net present cost (NPC), cost of energy (COE) and the renewable fraction (RF) were measured as performances metrics to compare the performances of different systems. For better optimisation, the model has been refined with a sensitivity analysis which explores performance variations due to solar irradiation and electricity prices. Finally, a statistical analysis was conducted to select the best potential places in Australia that produce maximum solar energy

Inverting Time-Dependent Harmonic Oscillator Potential by a Unitary Transformation and a New Class of Exactly Solvable Oscillators

A time-dependent unitary (canonical) transformation is found which maps the Hamiltonian for a harmonic oscillator with time-dependent real mass and real frequency to that of a generalized harmonic oscillator with time-dependent real mass and imaginary frequency. The latter may be reduced to an ordinary harmonic oscillator by means of another unitary (canonical) transformation. A simple analysis of the resulting system leads to the identification of a previously unknown class of exactly solvable time-dependent oscillators. Furthermore, it is shown how one can apply these results to establish a canonical equivalence between some real and imaginary frequency oscillators. In particular it is shown that a harmonic oscillator whose frequency is constant and whose mass grows linearly in time is canonically equivalent with an oscillator whose frequency changes from being real to imaginary and vice versa repeatedly.Comment: 7 pages, 1 figure include

Cerebellar Grey Matter Volumes in Reactive Aggression and Impulsivity in Healthy Volunteers

Several lines of evidence point towards the involvement of the cerebellum in reactive aggression. In addition to the posterior cerebellar hemisphere, the vermis has been suggested to play a prominent role in impulse regulation. In the present study, we set out to further examine the relationships between cerebellar grey matter volumes, aggression, and impulsivity in 201 healthy volunteers. 3 T structural magnetic resonance imaging scans were acquired to investigate grey matter volumes of the cerebellar vermis and the anterior and posterior lobules. Aggression was assessed with the Buss–Perry Aggression Questionnaire and impulsivity was measured with the Barratt Impulsiveness Scale-11. Results showed that impulsivity was positively associated with grey matter volumes of the cerebellar vermis and inversely correlated with grey matter volumes of the right posterior lobule. In addition, smaller volumes of the right posterior lobules were associated with higher physical aggression. Exploratory analyses indicated that for the right hemisphere, this association was driven by grey matter volumes of lobules VIIb and VIIIa. Our findings provide correlational evidence in healthy volunteers for the involvement of the cerebellar vermis and posterior lobules in a cortico-limbic-cerebellar circuit of aggression