Brant Stratman

Bio: Brant Stratman is an academic researcher from Vanderbilt University. The author has contributed to research in topics: Vibration fatigue & Statistical classification. The author has an hindex of 5, co-authored 7 publications receiving 253 citations.

Structural health monitoring of railroad wheels using wheel impact load detectors

Abstract: This paper proposes two quantitative criteria for removing railroad wheels from service, based on real-time structural health monitoring trends that are developed using data collected from trains while in service. The data is collected using wheel impact load detectors (WILDs). These impact load trends are able to distinguish wheels with a high probability of failure from high-impact wheels with a low probability of failure. The trends indicate the critical wheels that actually need to be removed, while at the same time allowing wheels that aren’t critical to remain in service. As a result, the safety of the railroad will be much improved by being able to identify and remove wheels that have high likelihood of causing catastrophic failures.

Improving railroad wheel inspection planning using classification methods

Abstract: Railroad wheel inspection attempts to identify failing wheels from a large population of wheels in service. This is a critical yet time consuming task. This paper presents a machine learning approach to automate the identification process using collected data from wheel inspection. Decision tree based and support vector machine based classification methods have been applied to the wheel inspection data analysis. A variation of the bagging ensemble approach is developed to improve the classification accuracy. The results of these methods achieve an identification accuracy of 80%. Analysis of the rules and models derived, as well as comparisons of the classification results obtained using the two base classification approaches are presented.

Identification of critical inspection samples among railroad wheels by similarity-based agglomerative clustering

Abstract: This paper proposes an unsupervised analysis methodfor identifying critical samples in large populations. The objective is to identify data features which help to pinpoint the critical samples that require the most inspection resources, namely time and money. Typically the data available for deriving the optimized inspection schedules in industry include both numeric and nominal features, and most clustering and classification algorithms are tailored for either numeric or nominal data. For this work, we adopt the Similarity-Based Agglomerative Clustering (SBAC) algorithm that has beenshown to be effective in clustering data with mixed numeric and nominal features. We present the effectiveness of this approach by applying it to an important problem in the railroadindustry, i.e., the inspection of railroad wheels.

A Review of Rolling Contact Fatigue

Abstract: Ball and rolling element bearings are perhaps the most widely used components in industrial machinery. They are used to support load and allow relative motion inherent in the mechanism to take place. Subsurface originated spalling has been recognized as one of the main modes of failure for rolling contact fatigue (RCF) of bearings. In the past few decades a significant number of investigators have attempted to determine the physical mechanisms involved in rolling contact fatigue of bearings and proposed models to predict their fatigue lives. In this paper, some of the most widely used RCF models are reviewed and discussed, and their limitations are addressed. The paper also presents the modeling approaches recently proposed by the authors to develop life models and better understanding of the RCF.

Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings

Abstract: Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC buildings will be time-consuming and complex. Recent research proved the artificial neural network (ANN) potentiality in solving various real-life problems. The traditional learning algorithms suffer from being trapped into local optima with a premature convergence. Thus, it is a challenging task to achieve expected accuracy while using traditional learning algorithms to train ANN. To solve this problem, the present work proposed a particle swarm optimization-based approach to train the NN (NN-PSO). The PSO is employed to find a weight vector with minimum root-mean-square error (RMSE) for the NN. The proposed (NN-PSO) classifier is capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. A database of 150 multistoried buildings’ RC structures was employed in the experimental results. The PSO algorithm was involved to select the optimal weights for the NN classifier. Fifteen features have been extracted from the structural design, while nine features have been opted to perform the classification process. Moreover, the NN-PSO model was compared with NN and MLP-FFN (multilayer perceptron feed-forward network) classifier to find its ingenuity. The experimental results established the superiority of the proposed NN-PSO compared to the NN and MLP-FFN classifiers. The NN-PSO achieved 90 % accuracy with 90 % precision, 94.74 % recall and 92.31 % F-Measure.

Recent applications of big data analytics in railway transportation systems: A survey

Abstract: Big data analytics (BDA) has increasingly attracted a strong attention of analysts, researchers and practitioners in railway transportation and engineering. This urges the necessity for a review of recent research development in this field. This survey aims to provide a comprehensive review of the recent applications of big data in the context of railway engineering and transportation by a novel taxonomy framework, proposed by Mayring (2003). The survey covers three areas of railway transportation where BDA has been applied, namely operations, maintenance and safety. Also, the level of big data analytics, types of big data models and a variety of big data techniques have been reviewed and summarized. The results of this study identify the existing research gaps and thereby directions of future research in BDA in railway transportation systems.