scholarly journals Application of Machine Learning Techniques for Railway Health Monitoring

2010 ◽  
pp. 396-421
Author(s):  
G.M. Shafiullah ◽  
Adam Thompson ◽  
Peter J. Wolfs ◽  
A.B.M. Shawkat Ali
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Vertical Acceleration ◽  
Lateral Instability ◽  
Learning Techniques ◽  
Safety And Reliability ◽  
Efficient Data ◽  
Modern Machine

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.

scholarly journals Application of Machine Learning Techniques for Railway Health Monitoring

2012 ◽  
pp. 2044-2067
Author(s):  
G.M. Shafiullah ◽  
Adam Thompson ◽  
Peter J. Wolfs ◽  
A.B.M. Shawkat Ali
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Vertical Acceleration ◽  
Lateral Instability ◽  
Learning Techniques ◽  
Safety And Reliability ◽  
Efficient Data ◽  
Modern Machine

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.

Delay Prediction of Aircrafts Based on Health Monitoring Data

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
B. A. Dattaram ◽  
N. Madhusudanan
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Flight Delay ◽  
Delay Prediction ◽  
The Relationship

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

scholarly journals Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 341-356
Author(s):  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Rolling Stock ◽  
Raw Data ◽  
Learning Techniques ◽  
Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

scholarly journals Machine learning for metagenomics: methods and tools

Metagenomics ◽  
2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Hayssam Soueidan ◽  
Macha Nikolski
Keyword(s):  
Machine Learning ◽  
Gene Prediction ◽  
Full Range ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Unified Framework ◽  
Comparative Metagenomics ◽  
Learning Techniques ◽  
Ngs Data ◽  
Modern Machine

AbstractOwing to the complexity and variability of metagenomic studies, modern machine learning approaches have seen increased usage to answer a variety of question encompassing the full range of metagenomic NGS data analysis.We review here the contribution of machine learning techniques for the field of metagenomics, by presenting known successful approaches in a unified framework. This review focuses on five important metagenomic problems:OTU-clustering, binning, taxonomic proffiing and assignment, comparative metagenomics and gene prediction. For each of these problems, we identify the most prominent methods, summarize the machine learning approaches used and put them into perspective of similar methods.We conclude our review looking further ahead at the challenge posed by the analysis of interactions within microbial communities and different environments, in a field one could call “integrative metagenomics”.

Big Data-Based Spectrum Sensing for Cognitive Radio Networks Using Artificial Intelligence

2020 ◽  
pp. 146-159 ◽  
Author(s):  
Suriya Murugan ◽  
Sumithra M. G.
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Spectrum Utilization ◽  
Candidate Solution ◽  
Learning Techniques ◽  
Efficient Data

Cognitive radio has emerged as a promising candidate solution to improve spectrum utilization in next generation wireless networks. Spectrum sensing is one of the main challenges encountered by cognitive radio and the application of big data is a powerful way to solve various problems. However, for the increasingly tense spectrum resources, the prediction of cognitive radio based on big data is an inevitable trend. The signal data from various sources is analyzed using the big data cognitive radio framework and efficient data analytics can be performed using different types of machine learning techniques. This chapter analyses the process of spectrum sensing in cognitive radio, the challenges to process spectrum data and need for dynamic machine learning algorithms in decision making process.

The Role of Uncertainty in Machine Learning As an Element of Control for Material Systems and Structures

2018 ◽  
Author(s):  
Muthu Ram Prabhu Elenchezhian ◽  
Md Rassel Raihan ◽  
Kenneth Reifsnider
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Composite Material ◽  
Health Monitoring ◽  
Crack Density ◽  
Regression Function ◽  
Fatigue Loading ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Material State

Recurrent neural networks (RNN) have been used to interpret data in situations wherein our knowledge of the active physics is incomplete. The currency of these methods is the data that are generated by a physical system. Unfortunately, if we are uncertain about the physics of the system, we also do not know the level of uncertainty in the data that we use to represent it. Typically, data provided to an RNN is provided by measurements of system state information, e.g., data that define speed, position, accelerations, configurations of system elements (like the flaps and elevators on an airplane) etc. But recently, data are being collected that indicate the state of the materials themselves that are used to construct the system. Material state may include the defect state of the materials such as the crack density and patterns in composite material in structural elements (obtained from health monitoring data). In this paper, we address the question of teaching a control system (e.g., for testing equipment, aircraft control systems, health monitoring systems, etc.) to recognize composite material degradation during service and to adjust applied loads and fields as part of a control scheme to avoid failure of the material during service. Topics will include defining a proper cost function for the above objectives, formulation of a ‘failure hypothesis’ as a regression function, and the quantification of uncertainty when the physics of the situation is not completely defined. Examples of machine learning techniques for a uniaxial fatigue loading of composite coupons with a circular hole are presented. Example models are random forest regression algorithms and artificial neural networks for linear regression.

scholarly journals Modern machine learning outperforms GLMs at predicting spikes

2017 ◽  
Author(s):  
Ari S. Benjamin ◽  
Hugo L. Fernandes ◽  
Tucker Tomlinson ◽  
Pavan Ramkumar ◽  
Chris VerSteeg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Activity ◽  
Linear Models ◽  
Feedforward Neural Networks ◽  
Predictive Performance ◽  
Machine Learning Techniques ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Neural Spiking ◽  
Modern Machine

AbstractNeuroscience has long focused on finding encoding models that effectively ask “what predicts neural spiking?” and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a GLM. Here we compared the predictive performance of GLMs to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. In general, the modern methods (particularly XGBoost and the ensemble) produced more accurate spike predictions and were less sensitive to the preprocessing of features. This discrepancy in performance suggests that standard feature sets may often relate to neural activity in a nonlinear manner not captured by GLMs. Encoding models built with machine learning techniques, which can be largely automated, more accurately predict spikes and can offer meaningful benchmarks for simpler models.

scholarly journals SPAHM: the Spectrum of Approximated Hamiltonian Matrices representations

2021 ◽  
Author(s):  
Clémence Corminboeuf ◽  
Alberto Fabrizio ◽  
Ksenia Briling
Keyword(s):  
Machine Learning ◽  
Learning Task ◽  
Real Space ◽  
Machine Learning Techniques ◽  
Chemical Information ◽  
Alternative Strategies ◽  
Learning Techniques ◽  
Alternative Approach ◽  
Quantum Machine Learning ◽  
Modern Machine

Abstract Physics-based molecular representations are the cornerstone of all modern machine learning techniques applied to solve chemical problems. While several approaches exist to design ever more accurate fingerprints, the majority resolves in including more physics to construct larger and more complex representations. Here, we present an alternative approach to harness the complexity of chemical information into a lightweight numerical form, naturally invariant under real-space transformations, and seamlessly including the information about the charge state of a molecule. The Spectrum of Approximated Hamiltonian Matrices (SPAHM) leverages the information contained in widely-used and readily-evaluated ``guess'' Hamiltonians to form a robust fingerprint for quantum machine learning. Relying on the origin of the SPAHM fingerprints and a hierarchy of approximate Hamiltonians, we analyze the relative merits of adding physics into molecular representations and find that alternative strategies, focusing more on the machine learning task, represent a clear route towards direct improvements.

scholarly journals Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques

2019 ◽  
Vol 9 (20) ◽  
pp. 4338 ◽  
Author(s):  
Hossein Moayedi ◽  
Dieu Tien Bui ◽  
Anastasios Dounis ◽  
Zongjie Lyu ◽  
Loke Kok Foong
Keyword(s):  
Machine Learning ◽  
Energy Efficient ◽  
Design Procedure ◽  
Absolute Error ◽  
Energy Performance ◽  
Machine Learning Techniques ◽  
Energy Efficient Buildings ◽  
Squared Error ◽  
Learning Techniques ◽  
Heating Load

The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings.

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