Prognostic and health management (PHM) can ensure that a lithium-ion battery is working safely and reliably. The main approach of PHM evaluation of the battery is to determine the State of Health (SoH) and the Remaining Useful Life (RUL) of the battery. The advancements of computational tools and big data algorithms have led to a new era of data-driven predictive analysis approaches, using machine learning algorithms. This paper presents the preliminary development of the data-driven prognostic, using a Deep Neural Networks (DNN) approach to predict the SoH and the RUL of the lithium-ion battery. The effectiveness of the proposed approach was implemented in a case study with a battery dataset obtained from the NASA Ames Prognostics Center of Excellence (PCoE) database. The proposed DNN algorithm was compared against other machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Linear Regression (LR). The experimental results reveal that the performance of the DNN algorithm could either match or outweigh other machine learning algorithms. Further, the presented results could serve as a benchmark of SoH and RUL prediction using machine learning approaches specifically for lithium-ion batteries application.

Predicting Remaining Useful Life (RUL) of systems has played an important role in various fields of reliability engineering analysis, including in aircraft engines. RUL prediction is critically an important part of Prognostics and Health Management (PHM), which is the reliability science that is aimed at increasing the reliability of the system and, in turn, reducing the maintenance cost. The majority of the PHM models proposed during the past few years have shown a significant increase in the amount of data-driven deployments. While more complex data-driven models are often associated with higher accuracy, there is a corresponding need to reduce model complexity. One possible way to reduce the complexity of the model is to use the features (attributes or variables) selection and dimensionality reduction methods prior to the model training process. In this work, the effectiveness of multiple filter and wrapper feature selection methods (correlation analysis, relief forward/backward selection, and others), along with Principal Component Analysis (PCA) as a dimensionality reduction method, was investigated. A basis algorithm of deep learning, Feedforward Artificial Neural Network (FFNN), was used as a benchmark modeling algorithm. All those approaches can also be applied to the prognostics of an aircraft gas turbine engines. In this paper, the aircraft gas turbine engines data from NASA Ames prognostics data repository was used to test the effectiveness of the filter and wrapper feature selection methods not only for the vanilla FFNN model but also for Deep Neural Network (DNN) model. The findings show that applying feature selection methods helps to improve overall model accuracy and significantly reduced the complexity of the models.

In this day and age, a battery is a promising electrical power storage system for various engineering applications. The role of prognostic and health management (PHM) in battery application is to ensure that the battery system is reliable and safe through the estimation of State of Health (SoH) and Remaining Useful Life (RUL) of the battery. Although there is a growing research trend in predicting the SoH and RUL using various machine learning algorithms, there has not yet been many sophisticated works that employ the deep learning algorithms for PHM in battery analysis.

The majority of the Prognostics and Health Management (PHM) models proposed during the past few years have shown a significant increase in the amount of data-driven deployments. While more complex data-driven models are often associated with higher accuracy, there is a corresponding need to reduce model complexity. One possible way to reduce the complexity of the model is to use the feature (attribute or variable) selection and dimensionality reduction methods prior to the model training process. In this work, the effectiveness of multiple filter and wrapper feature selection methods (correlation analysis, relief forward/backward selection and etc.), along with Principal Component Analysis (PCA) as a dimensionality reduction method, was investigated. A basis algorithm of deep learning, Feedforward Artificial Neural Network (FFNN), was used as a benchmark modeling algorithm. As a case study, the prognostics of aircraft engine data from NASA Ames prognostics data repository was used to test the effectiveness of the filter and wrapper feature selection methods. The findings show that applying feature selection methods helps to improve model accuracy and significantly reduced the complexity of the models. This study hopes to be a baseline assumption for future complexity reduction solutions that can help in improving the deep learning approaches employed in the PHM domain.

Diesel fuel price is generally considered to be one of the core factors that contributes to the cost of transportation and logistics activities. However, stand-alone time-series algorithms without considering other related logistics attributes may not be sufficient to construct well-defined diesel price-prediction models. This work used artificial Deep Neural Networks (DNN s) to construct prediction models with consideration of the Road Freight Transport Index (RFTI) and the Baltic Dry Index (BDI) as model attributes. The results from regression models with BDI and RFTI attributes were compared against models employing traditional time-series algorithms, such as moving average, Autoregressive Integrated Moving Average (ARIMA), exponential smoothing, and seasonal forecasting. Proposed diesel price prediction models with consideration of BDI and RFTI can perform better than traditional time series in the end.