Advances in Data Science and Adaptive Analysis

In this paper, a fault prediction method for oil well equipment based on the analysis of time series data obtained from multiple sensors is proposed. The proposed method is based on deep learning (DL). For this purpose, comparative analysis of single-layer long short-term memory (LSTM) with the convolutional neural network (CNN) and stacked LSTM methods is provided. To demonstrate the efficacy of the proposed method, some experiments are conducted on the real data set obtained from eight sensors installed in oil wells. In this paper, compared to the single-layer LSTM model, the CNN and stacked LSTM predicted the faulty time series with a minimal loss.

For the data fitting problem under interval uncertainty, we introduce the concept of strong compatibility between data and parameters. It is shown that the new strengthened formulation of the problem reduces to computing and estimating the so-called tolerable solution set for interval systems of equations constructed from the data being processed. We propose a computational technology for constructing a “best-fit” linear function from interval data, taking into account the strong compatibility requirement. The properties of the new data fitting approach are much better than those of its predecessors: strong compatibility estimates have polynomial computational complexity, the variance of the strong compatibility estimates is almost always finite, and these estimates are rubust. An example considered in the concluding part of the paper illustrates some of these features.

Highway-rail grade crossing (HRGC) accidents continue to be a major source of transportation casualties in the United States. This can be attributed to increased road and rail operations and/or lack of adequate safety programs based on comprehensive HRGC accidents analysis amidst other reasons. The focus of this study is to predict HRGC accidents in a given rail network based on a machine learning analysis of a similar network with cognate attributes. This study is an improvement on past studies that either attempt to predict accidents in a given HRGC or spatially analyze HRGC accidents for a particular rail line. In this study, a case for a hybrid machine learning and geographic information systems (GIS) approach is presented in a large rail network. The study involves collection and wrangling of relevant data from various sources; exploratory analysis, and supervised machine learning (classification and regression) of HRGC data from 2008 to 2017 in California. The models developed from this analysis were used to make binary predictions [98.9% accuracy & 0.9838 Receiver Operating Characteristic (ROC) score] and quantitative estimations of HRGC casualties in a similar network over the next 10 years. While results are spatially presented in GIS, this novel hybrid application of machine learning and GIS in HRGC accidents’ analysis will help stakeholders to pro-actively engage with casualties through addressing major accident causes as identified in this study. This paper is concluded with a Systems-Action-Management (SAM) approach based on text analysis of HRGC accident risk reports from Federal Railroad Administration.

In regression for noisy inputs, noise is typically removed from a given noisy input if possible, and then the resulting noise-free input is provided to the regression function. In some cases, however, there is no available time or method for removing noise. The regression method proposed in this paper determines a regression function for noisy inputs using the estimated posterior of their noise-free constituents with a nonparametric estimator for noiseless explanatory values, which is constructed from noiseless training data. In addition, a probabilistic generative model is presented for estimating the noise distribution. This enables us to determine the noise distribution parametrically from a single noisy input, using the distribution of the noise-free constituent of noisy input estimated from the training data set as a prior. Experiments conducted using artificial and real data sets show that the proposed method suppresses the overfitting of the regression function for noisy inputs and the root mean squared errors (RMSEs) of the predictions are smaller compared with those of an existing method.