Fault diagnosis and prognosis are some of the most crucial functionalities in complex and safety-critical engineering systems, and particularly fault diagnosis, has been a subject of intensive research in the past four decades. Such capabilities allow for detection and isolation of early developing faults as well as prediction of fault propagation, which can allow for preventive maintenance, or even serve as a countermeasure to the possibility of catastrophic incidence as a result of a failure. Following a short preliminary overview and definitions, this article provides a survey of recent research on fault prognosis. Additionally, we report on some of the significant application domains where prognosis techniques are employed. Finally, some potential directions for future research are outlined.

As wind energy is becoming a significant utility source, minimizing the Operation and Maintenance (O&M) expenses has raised a crucial issue to make wind energy competitive to fossil fuels. Wind Turbines (WTs) are subject to unexpected failures due to operational and environmental conditions, aging, etc. An accurate estimation of time to failures assures reliable power production and lower maintenance costs. In recent years, a notable amount of research has been undertaken to propose prognosis techniques that can be employed to forecast the Remaining Useful Life (RUL) of wind farm assets. This paper provides a recent literature review on modeling developments for the remaining useful life prediction of critical WT components including physics-based, Artificial Intelligence (AI)-based, stochastic-based, and hybrid prognostics. In particular, the pros and cons of the prognosis models are investigated to assist researchers in selecting proper models for certain applications of WT RUL forecast. Our comprehensive review has revealed that hybrid methods are now the leading and most accurate tools for WT failure predictions over individual hybrid components. Strong candidates for future research include the consideration of variable operating environments, component interaction, physicsbased prognostics, and Bayesian application in the development of WT prognosis methods.

This paper introduces a new condition monitoring approach for extracting fault signatures in wind turbine blades by utilizing the data from a real-time Supervisory Control and Data Acquisition (SCADA) system. A hybrid fault detection system based on a combination of Generalized Regression Neural Network Ensemble for Single Imputation (GRNN-ESI) algorithm, Principal Component Analysis (PCA), and wavelet-based Probability Density Function (PDF) approach is proposed in this work. The proposed fault detection strategy accurately detects incipient blade failures and leads to improved maintenance cost and availability of the system. Experimental test results based on data from a wind farm in southwestern Ontario, Canada, illustrate the effectiveness and high accuracy of the proposed monitoring approach.

Aircrafts are complex engineering systems composed of many interconnected subsystems with possible uncertainties in their structure. They often function for long number of flight hours under varying or harsh environments. Hence, Prognostic and Health Management (PHM) of critical subsystems or components within the overall system is crucial for maintaining the safety and reliability of the aircraft. This paper reviews the state-of-the-art in aircraft failure prognostic. Main definitions and concepts are presented and discussed. In addition, a selected important failures in the representative aircraft components are outlined, and various categories of prognostic strategies are reviewed. Finally, some recommendations and directions for the most promising research to address the PHM problem in aircraft are outlined.

In this paper, a new fusion scheme based on the Dempster–Shafer Evidence Theory (DSET) is introduced for Epileptic Seizure Detection (ESD) in brain disorders. Firstly, various features in temporal, spectral, and temporal-spectral domains are extracted from Electroencephalogram (EEG) signals. Afterward, a Correlation analysis via the Pearson Correlation Coefficient (PCC) is conducted on the extracted features to select and remove highly correlated features. It leads to the second feature set with about half numbers of the first feature set. Next, three separate filter-type feature selection techniques, including Relief-F (RF), Compensation Distance Evaluation Technique (CDET), and Fisher Score (FS), are conducted to this second feature set for ranking features. Following that, a feature fusion is engaged by the DSET through the individual feature ranking results to generate high qualified feature sets. Indeed, the DSET-based feature fusion is devoted to enhancing the feature selection confidence using the least superb ranked features. In the classification stage, an Ensemble Decision Tree (EDT) classifier, along with two common validation procedures, including hold out and 10-fold cross-validation, is appropriated to classify the selected features from the EEG signals as normal, pre-ictal (epileptic background), and ictal (epileptic seizure) classes. Finally, several test scenarios are investigated using experimental data of Bonn University to evaluate the proposed ESD performance. Moreover, a comparison with other research works on the same dataset and classes is accomplished. The obtained results indicate the effectiveness of the proposed feature fusion approach and superior accuracy compared to the traditional methods.