In light of the advances in big data, high-dimensional datasets are often encountered. Incorporating them into data-driven models can enhance performance; however, this comes at the cost of high computation and the risk of overfitting, particularly due to abundant redundant features. Identifying an informative subset of the features helps in reducing the dimensionality and enhancing model interpretability. In this paper, we propose a novel framework for unsupervised feature selection, called Selective Deep Auto-Encoder (SDAE). It aims to reduce the number of features used in unlabeled datasets without compromising the quality of information obtained. It achieves this by selecting sufficient features - from the original feature set - capable of representing the entire feature space and reconstructing them. Architecturally, it leverages the use of highly nonlinear latent representations in deep Autoencoders and intrinsically learns, in an unsupervised fashion, the relevant and globally representative subset of features through a customized Selective Layer. Extensive experimental results on three high-dimensional public datasets have shown promising feature selection performance by SDAE in comparison to other existing state-of-the-art unsupervised feature selection methods.

For the safe and reliable operation of battery-driven machines, accurate state-of-charge (SOC) estimations are necessary. Unfortunately, existing methods often fail to identify patterns relevant to long-term SOC estimation due to complex battery cell characteristics such as aging. In this paper, we propose the Uncorrelated Sparse Autoencoder with Long Short-Term Memory (USAL). USAL is a novel neural network that addresses the challenging task of long-term SOC estimation given a limited initial history of a cell’s charge-discharge behavior. USAL uses a multi-task learning strategy to harness the advantages of sparse autoencoders and Long Short-term Memory (LSTM) networks by enforcing correlation penalties. The USAL simultaneously learns to (i) generate a latent space of informative SOC encodings from commonly measured cell characteristics, (ii) penalize for high multicollinearity between encodings, and (iii) identify non-trivial long and short temporal correlations between the encodings using LSTM cells. USAL outperforms benchmarked models in our experiments when trained on five initial charge-discharge cycles across multiple battery cells using three publicly available accelerated aging datasets. Note to Practitioners—This paper proposes USAL, a custom-built deep neural network to address the challenging task of long-term SOC estimations in battery cells. Long-term SOC estimation involves estimating SOC for cycles near End-Of-Life (EOL) given some initial charge-discharge cycles. Three fundamental steps involved in long-term SOC estimations using USAL are (i) exploiting a multi-task learning strategy to learn efficient encodings given limited training data, (ii) penalizing these encodings for high correlations to efficiently transform measured inputs into a space of informative features, and (iii) mapping of aging-related trends to support long-term SOC estimations. USAL is designed to be a data-driven SOC estimation method that is (i) capable of alerting the user to a faulty cell when integrated into a real-life Battery Management System (BMS) and (ii) identifying the relative quality of a battery cell from only a few initial charge-discharge cycles.

Self-driving vehicles promise many safety, mobility, and environmental benefits. However, users’ lack of trust and acceptance may threaten the success and potential of this technology. Monitoring the driver’s emotional state is one way to address this challenge. Empathetic automation can respond to the driver’s state and improve the experience and acceptance of self-driving vehicle drivers. In this study, 24 participants rode in a self-driving vehicle simulator and experienced three automation styles (aggressive, moderate, conservative) and four intersection types (with and without a stop sign, and with and without traffic.) We identified the observed drivers’ emotions from the video data and labeled the video frames using the dimensional and discrete emotion models to examine how automation behavior affects the driver’s emotional state. We used multilevel Bayesian linear regression and multilevel Dirichlet regression to model the continuous and discrete emotions, respectively. The automation driving style effect varied for each participant. The same conditions provoked positive responses for some participants, and negative for others. Furthermore, the results showed that intersection type, the position within the intersection, and their interaction affected the driver’s emotional state. This indicates that personalized driver state monitoring systems might enhance drivers’ experience in self-driving vehicles.

The rapid development of information technologies provided unprecedented big data environments for condition monitoring and degradation analyses. However, the available big data sets are often sparse with a limited number of observations per recorded unit. For example, in many healthcare systems, data are collected from a large number of patients, but the available observations from each patient are quite limited. Unfortunately, most of the existing approaches for data-driven degradation modeling may not work well in this scenario as they either pool the information from the population or require rich historical observations in each unit. To address the challenges in “sparse data environments,” this paper proposes a structural degradation modeling framework (SDM). The SDM is inspired by the recommender system, which provides recommendations about specific items for the user. In addition, it is also tailored to the needs of degradation modeling. In particular, the framework takes into consideration: 1) the available data from the unit of interest; 2) the population characteristics; 3) the relationship between the available units; and 4) the precision of the available units. Simulation studies and a case study that involves the Alzheimer's disease (AD) neuroimaging initiative data set are conducted, which shows satisfactory performance of the proposed method.

Due to the rapid development of sensing technologies, multiple sensors became available for real-time monitoring of the degradation status of machine systems. With such a wealth of data collected from multiple sensors, researchers have proposed different sensor fusion approaches for prognosis and condition monitoring, thus allowing accurate inference of the remaining useful life (RUL) of machine systems. However, almost no method provides a statistical metric to evaluate the quality of degradation signals for prognosis and condition monitoring. To fill this literature gap, this paper develops a sensor fusion framework to check the reliability of given degradation signals for prognosis and degradation analysis through a series of statistical hypothesis tests. A health index is constructed in the sensor fusion framework to help differentiate between distinct degradation states. Based on the health index, the remaining times to various degradation states are estimated, including the RUL to failure. A published degradation data set of aircraft engines is used to evaluate and compare the prognostic and condition monitoring performance of the proposed method with benchmark methods.

Due to the rapid development of sensing and computing technologies, multiple sensors have been widely used in a system to simultaneously monitor the health status of an operating unit. Such a data-rich environment creates an unprecedented opportunity to better understand the degradation behavior of the system and make accurate inferences about the remaining lifetime. Since data collected from multiple sensors are often correlated and each sensor data contains only partial information about the degraded unit, data fusion methodologies that integrate the data from multiple sensors provide an essential tool for degradation modeling and prognostics. To achieve this goal, a fundamental question needs to be answered first is how to measure the signal quality of a degradation signal. If such a question can be addressed, then the data fusion approach can be simplified as a mission-specific task: to construct a composite health index with the goal of optimizing its signal quality. In this paper, a new signal-to-noise ratio (SNR) metric that is tailored to the needs of degradation signals is proposed. Then, based on the new quality metric, we develop a data-level fusion model to construct a health index via fusion of multiple degradation-based sensor data. Our goal is that the developed health index provides a much better characterization of the health condition of the unit and thus leads to a better prediction of the remaining lifetime. A case study that involves the degradation dataset of aircraft gas turbine engines is conducted to numerically evaluate the performance of the developed health index regarding prognostics and further compare the result with existing literature.

The rapid development of sensor and computing technology has created an unprecedented opportunity for condition monitoring and prognostic analysis in various manufacturing and healthcare industries. With the massive amount of sensor information available, important research efforts have been made in modeling the degradation signals of a unit and estimating its remaining useful life distribution. In particular, a unit is often considered to have failed when its degradation signal crosses a predefined failure threshold, which is assumed to be known a priori. Unfortunately, such a simplified assumption may not be valid in many applications given the stochastic nature of the underlying degradation mechanism. While there are some extended studies considering the variability in the estimated failure threshold via data-driven approaches, they focus on the failure threshold distribution of the population instead of that of an individual unit. Currently, the existing literature still lacks an effective approach to accurately estimate the failure threshold distribution of an operating unit based on its in-situ sensory data during condition monitoring. To fill this literature gap, this paper develops a convex quadratic formulation that combines the information from the degradation profiles of historical units and the in-situ sensory data from an operating unit to online estimate the failure threshold of this particular unit in the field. With a more accurate estimation of the failure threshold of the operating unit in real time, a better remaining useful life prediction is expected. Simulations as well as a case study involving a degradation dataset of aircraft turbine engines were used to numerically evaluate and compare the performance of the proposed methodology with the existing literature in the context of failure threshold estimation and remaining useful life prediction.