Berghout TarekUniversité Batna 2 · Industrial Engineering
Berghout Tarek
PhD
Looking for a postdoc or any research-related jobs in "machine learning applications"
About
40
Publications
18,681
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230
Citations
Citations since 2017
Introduction
I work on developing machine learning algorithms for CONDITION MONITORING (mainly prognosis) and CYBERSECURITY. I've discovered a few and I'm struggling to find others. It is important to me that my papers are interesting to read. I don't like to just throw hard math and boring speech at readers. My motto: "It is better to fail doing you own work than to succeed in imitating the work of others".
Publications
Publications (40)
Electricity theft, known as "Non-Technical Loss" (NTL) is certainly one of the priorities of power distribution utilities. Indeed, NTL could lead to serious damage ranging from massive financial losses to loss of reputation resulting from poor power quality. With advances in metering infrastructure technologies, the availability of user data has fu...
Lithium-ion (Li-ion) batteries play an important role in providing necessary energy when acting as a main or backup source of electricity. Indeed, the unavailability of battery aging discharge data in most real-world applications makes the State of Health (SoH) assessment very challenging. Alternatively, accelerated aging is therefore adopted to em...
Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends not only on the availability of labeled historical data but also on the careful samples selection. However, in real operating sys...
Reliability and security of power distribution and data traffic in smart grid (SG) are very important for industrial control systems (ICS). Indeed, SG cyber-physical connectivity is subject to several vulnerabilities that can damage or disrupt its process immunity via cyberthreats. Today's ICSs are experiencing highly complex data change and dynami...
Remaining Useful Life (RUL) prediction for aircraft engines based on the available run-to-failure measurements of similar systems becomes more prevalent in Prognostic Health Management (PHM) thanks to the new advanced methods of estimation. However, feature extraction and RUL prediction are challenging tasks, especially for data-driven prognostics....
The attached “Train.gif” and “Test.gif” files represents the prepared version of the New commercial modular Aeropropulsion and System Simulation (N-CMAPSS) dataset published in [1]. The preparation process follows methodology proposed in this paper [2] while codes are available at [3].
References
[1] M. Arias Chao, C. Kulkarni, K. Goebel, and O....
This paper presents PrognosEase; a software that provides an easier way to produce different types of run-to-failure data mimicking real-world conditions to simplify prognosis studies in terms of data collection and improvement in ML degradation modelling process. Different types of degradation types made available to meet different types of applic...
Data-driven prognostics and health management (PHM) is key to increasing the productivity of industrial processes through accurate maintenance planning. The increasing complexity of the systems themselves, in addition to cyber-physical connectivity, has brought too many challenges for the discipline. As a result, data complexity challenges have bee...
Advanced technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), underpin many of the innovations in Industry 4.0. However, the interconnectivity and open nature of such systems in smart industrial facilities can also be targeted and abused by malicious actors, which reinforces the importance of cyber security. In this...
Condition monitoring (CM) of industrial processes is essential for reducing downtime and increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling. Indeed, advanced intelligent learning systems for Fault Diagnosis (FD) make it possible to effectively isolate and identify the origins of faults. Proven smart industrial inf...
Machine learning prognosis for condition monitoring of safety-critical systems such as aircraft engines, continually, faces challenges of data unavailability, complexity, and drift. Consequently, this paper overcomes these challenges by introducing adaptive deep transfer learning methodologies strengthened with robust feature engineering. At first...
Smart grid is an emerging system providing many benefits and ease in digitizing the traditional power distribution systems. However, the added benefits of digitization and the use of internet of things (IoT) technologies in smart grid also poses threats to its reliable continuous operation due to cyberattacks. Cyber-physical smart grid systems must...
The clean energy conversion characteristics of proton exchange membrane fuel cells (PEMFCs) have given rise to many applications, particularly in transportation. Unfortunately, the commercial application of PEMFCs is hampered by the early deterioration and low durability of the cells. In this case, accurate real-time condition monitoring plays an i...
The increase in electricity theft has become one of the main concerns of power distribution networks. Indeed, electricity theft could not only lead to financial losses but also leads to reputation damage by reducing the quality of supply. With advanced sensing technologies of metering infrastructures, data collection of electricity consumption enab...
Federated learning (FL) is a data-privacy-preserving, decentralized process that allows local edge devices of smart infrastructures to train a collaborative model independently while keeping data localized. FL algorithms, encompassing a well-structured average of the training parameters (e.g., the weights and biases resulting from training-based st...
Green conversion of proton exchange membrane fuel cells (PEMFCs) has received particular attention in both stationary and transportation applications. However, the poor durability of PEMFC represents a major problem that hampers its commercial application since dynamic operating conditions, including physical deterioration, have a serious impact on...
In modern Smart Grids (SGs) ruled by advanced computing and networking technologies, condition monitoring relies on secure cyberphysical connectivity. Due to this connection, a portion of transported data, containing confidential information, must be protected as it is vulnerable and subject to several cyber threats. SG cyberspace adversaries attem...
Due to advanced monitoring technologies including the plug-in of the cyber and physical layers on the Internet, cyber-physical systems are becoming more vulnerable than ever to cyberthreats leading to possible damage of the system. Consequently, many researchers have devoted to studying detection and identification of such threats in order to mitig...
![Figure 5. Illustration of the PRONOSTIA platform [25].](profile/Berghout-Tarek/publication/359578507/figure/fig10/AS:1140257613455368@1648870008116/Illustration-of-the-PRONOSTIA-platform-25_Q320.jpg)
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...
Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it...
Graphical Abstract for the following paper:
https://www.researchgate.net/publication/354961202_Machine_Learning-Based_Condition_Monitoring_for_PV_Systems_State_of_the_Art_and_Future_Prospects
One of the main data-driven challenges when assessing bearing health is that training and test samples must be drawn from the same probability distribution. Indeed, it is difficult and almost rare to witness such a phenomenon in practical applications due to the constantly changing working conditions of rotating machines. In addition, collecting su...
Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile found...
In order to ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. In fact, the continuous uniform delivery of such energy depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurred faults coul...
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 im...
“Predicting from the Hidden Layer of a Neural Network!!!???” It's weird, isn't it? Actually this is a very interesting idea; we have developed a new original autoencoder that allows hiding labels inside the hidden layer to predict from it later, rather than the output layer in a completely unsupervised approximation. The idea applies to both numeri...
Since, bearings deterioration patterns are difficult to be collected from real long life time scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, the insufficient recovered features due to the rapid damage propagation seem more likely to lead to poorly generalized learning mach...
In the last years, predictive maintenance has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic models for health assessment based on available historical labeled data issued from similar systems or specific physical models. Ho...
These files contains the comparative studdy shown in original papre of regularized length changeable ELM .
The main contribution of this paper is to introduce a new iterative training algorithm for restricted Boltzmann machines. The proposed learning path is inspired from online sequential extreme learning machine one of extreme learning machine variants which deals with time accumulated sequences of data with fixed or varied sizes. Recursive least squa...
This work can be considered as a first step of designing a future competitive data-driven approach for remaining useful life prediction of aircraft engines. The proposed approach is an ensemble of serially connected extreme learning machines. The results of prediction of the first networks are scaled and fed to the next networks as an additive feat...
In this work a new data-driven compression approach is
presented. The compression algorithm is an autoencoder
trained with an improved On-line sequential Extreme
Learning Machine (OS-ELM). First, a dynamic
adaptation of the training algorithm towards the newly
coming data is achieved by integrating an updated
selection strategy (USS) and dynamic fo...
In this work a new data-driven approach for Remaining
Useful Life estimation of aircraft engines is developed.
The proposed approach is a regularized Single Hidden
Layer Feedforward Neural network (SLFN) with
incremental constructive enhancements. The training
rules of this algorithm are inspired form different
Extreme Learning Machine (ELM) varian...
the main objective of this works is to study and improve the performances of the Single hidden Layer Feedforward Neural network (SLFN) for the application of Remaining Useful Life (RUL) prediction of aircraft engines. The most common problems in SLFNs based old training algorithms such as backpropagation are time consuming, over-fitting and the app...
the zip file contains the source code of the proposed approach (Temporal difference stacked online sequential extreme learning machine). The one can use these codes for developping or enhancing new data-driven approaches. A GUI toolbox is integrated to facilitate testing of the proposed Approch.
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 dimensio...
MATLAB codes for : Dynamic Adaptation for Length Changeable Weighted Extreme Learning Machine
In this paper, a new length changeable extreme learning machine is proposed. The aim of the proposed method is to improve the learning performances of a Single hidden layer feedforward neural network (SLFN) under rich dynamic imbalanced data. Particle Swarm Optimization (PSO) is involved for hyper-parameters tuning and updating during incremental l...
this presentation discribs new developed software based on gui MATLAB for studying machine learning algorithms
Questions
Questions (4)
What are main features that makes able to know that any designed network is either a deep network or a small scale machine learning?
Projects
Projects (2)
condition monitoring-based machine learning is usually a topic of data availability, complexity, and drift. Advances in cyber-physical connectivity extend these challenges towards decentralized learning challenges while data privacy plays an undeniably very important role. Hence, federated learning comes in an attempt to provide a decentralized generalized learning process without data sharing. In this context, the goal of this project is to track recent advances in machine learning in condition monitoring while considering both traditional learning challenges and federated learning challenges namely:
- Data complexity
- Data unavailability
- Data drift
- Statistical heterogeneity
- Systems heterogeneity
- Communication efficiency
- Privacy
For the next Annual Conference of the IEEE Industrial Electronics Society (IECON 2022), please consider submitting papers to the Special Session SS16 on “Machine Learning for Cyberphysical Security and Resilience in Smart Grids”:
https://iecon2022.org/
This Special Session, organized by Mohamed Benbouzid (University of Brest), Mohamed Amine Ferrag (University of Guelma), and Tarek Berghout (university Batna 2), is supported by the Renewable Energy Systems Technical Committee of the IEEE IES (https://res.ieee-ies.org/).
