Tarek Berghout

Bio: Tarek Berghout is an academic researcher from University of Batna. The author has contributed to research in topics: Computer science & Context (archaeology). The author has an hindex of 4, co-authored 10 publications receiving 36 citations.

Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

Abstract: The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

Abstract: Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

A Systematic Guide for Predicting Remaining Useful Life with Machine Learning

Abstract: Prognosis and health management (PHM) are mandatory tasks for real-time monitoring of damage propagation and aging of operating systems during working conditions. More definitely, PHM simplifies conditional maintenance planning by assessing the actual state of health (SoH) through the level of aging indicators. In fact, an accurate estimate of SoH helps determine remaining useful life (RUL), which is the period between the present and the end of a system’s useful life. Traditional residue-based modeling approaches that rely on the interpretation of appropriate physical laws to simulate operating behaviors fail as the complexity of systems increases. Therefore, machine learning (ML) becomes an unquestionable alternative that employs the behavior of historical data to mimic a large number of SoHs under varying working conditions. In this context, the objective of this paper is twofold. First, to provide an overview of recent developments of RUL prediction while reviewing recent ML tools used for RUL prediction in different critical systems. Second, and more importantly, to ensure that the RUL prediction process from data acquisition to model building and evaluation is straightforward. This paper also provides step-by-step guidelines to help determine the appropriate solution for any specific type of driven data. This guide is followed by a classification of different types of ML tools to cover all the discussed cases. Ultimately, this review-based study uses these guidelines to determine learning model limitations, reconstruction challenges, and future prospects.

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

Abstract: To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

Deep Learning for Computer Vision

Abstract: Deep Learning is not just a keyword abuzz in the industry and academics alike, it has thrown open a whole new field of possibilities. Deep Learning models are being employed in all sorts of use cases and domains, some of which we saw in the previous chapters. Artificial neural networks have tremendous potential to learn complex non-linear functions, patterns, and representations and their power is driving research in multiple fields, including computer vision, audio-visual analysis, chatbots and natural language understanding, to name a few. In this chapter, we touch on some of the advanced areas in the field of computer vision, which have recently come into prominence with the advent of Deep Learning. This includes real-world applications like image categorization and classification and they very popular concept of image artistic style transfer. Computer vision is all about the art and science of making machines understand high-level useful patterns and representations from images and video so that it would be able to make intelligent decisions similar to what a human would do upon observing its surroundings. Building on core concepts like convolutional neural networks and transfer learning, this chapter provides you with a glimpse into the forefront of Deep Learning research with several real-world case studies from computer vision.

A Novel Transfer Learning Approach in Remaining Useful Life Prediction for Incomplete Dataset

Abstract: Due to the successful implementation of intelligent data-driven approaches, these methods are gaining remarkable attention in predicting the remaining useful life (RUL) problems. Within this scope, transfer learning approaches are exploited to transfer the obtained knowledge from the source domain data to the target domain data. Due to the different working regimes and operating conditions, there exists a discrepancy between the data distribution of source and target domain datasets. Domain adaptation techniques are deployed to tackle the data distribution discrepancy. In most prognostic problems, it is assumed that the complete life-cycle run-to-failure information for the target domain dataset is available. However, in real-practical scenarios, providing complete life-cycle data is not straightforward. To solve this issue, this article proposed a transfer learning approach for RUL prediction using a consistency-based regularization. In the proposed deep learning framework, a consistency-based regularization term is added to the objective function to remove the negative effect of missing information in the incomplete target domain dataset. In order to further validate the effectiveness of the proposed method, a comprehensive experimental analysis has been done on two different aerospace and bearing datasets.