Hadi Harb

Data Analysis for the Aero Derivative Engines Bleed System Failure Identification and Prediction

International journal of intelligent systems and applications, Dec 8, 2021

Middle size gas/diesel aero-derivative power generation engines are widely used on various indust... more Middle size gas/diesel aero-derivative power generation engines are widely used on various industrial plants in the oil and gas industry. Bleed of Valve (BOV) system failure is one of the failure mechanisms of these engines. The BOV is part of the critical anti-surge system and this kind of failure is almost impossible to identify while the engine is in operation. If the engine operates with BOV system impaired, this leads to the high maintenance cost during overhaul, increased emission rate, fuel consumption and loss in the efficiency. This paper proposes the use of readily available sensor data in a Supervisory Control and Data Acquisition (SCADA) system in combination with a machine learning algorithm for early identification of BOV system failure. Different machine learning algorithms and dimensionality reduction techniques are evaluated on real world engine data. The experimental results show that Bleed of Valve systems failures could be effectively predicted from readily available sensor data.

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Classification du signal sonore en vue d'une indexation par le contenu des documents multimédia

Les humains ont une aptitude remarquable de classifier les signaux sonores en des classes : musiq... more Les humains ont une aptitude remarquable de classifier les signaux sonores en des classes : musique, parole, applaudissement, explosion, etc. . . Face a une profusion de documents multimedias, nous proposons dans cette these de developper des machines de perception auditive automatiques dotees des capacites de classification sonore similaires a celles des humains. Nous suivons une approche inspiree par quelques etudes sur la perception humaine des classes semantiques sonores du contexte de multimedia et nous proposons ainsi un modele de memoire sonore qui est a la base d'un espace de representation du signal, nomme espace MGI (Modelisation Gaussienne Incrementale). Un comite de reseaux de neurones est couple a l'espace MGI afin de constituer un systeme de classification du signal sonore. Ce systeme a permis efficacement la classification en musique/parole, homme/femme, action/non-action, genre de musique, ainsi qu'une structuration des videos en des "scene sonores".

Automatically Structuring a Video into Audio Scenes and Audio Chapters

HAL (Le Centre pour la Communication Scientifique Directe), Apr 21, 2004

This paper introduces audio scenes and audio chapters in movies and presents an efficient algorit... more

Highlights Detection in Sports Videos Based on Audio Analysis

HAL (Le Centre pour la Communication Scientifique Directe), Sep 22, 2003

... A system that indexes in real time tennis videos using player and ball tracking is presented ... more

Data Analysis for the Aero Derivative Engines Bleed System Failure Identification and Prediction

International Journal of Intelligent Systems and Applications, 2021

Middle size gas/diesel aero-derivative power generation engines are widely used on various indust... more Middle size gas/diesel aero-derivative power generation engines are widely used on various industrial plants in the oil and gas industry. Bleed of Valve (BOV) system failure is one of the failure mechanisms of these engines. The BOV is part of the critical anti-surge system and this kind of failure is almost impossible to identify while the engine is in operation. If the engine operates with BOV system impaired, this leads to the high maintenance cost during overhaul, increased emission rate, fuel consumption and loss in the efficiency. This paper proposes the use of readily available sensor data in a Supervisory Control and Data Acquisition (SCADA) system in combination with a machine learning algorithm for early identification of BOV system failure. Different machine learning algorithms and dimensionality reduction techniques are evaluated on real world engine data. The experimental results show that Bleed of Valve systems failures could be effectively predicted from readily avail...

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Classification and

of experts for audio classification: an application to male female

Optimization of a Rfid Network Planning ( RNP ) Model Using a Multi-Objective Hybrid Evolutionary Algorithm

Neural Networks-based Process Model and its Integration with Conventional Drum Level PID Control in a Steam Boiler Plant

International Journal of Engineering and Manufacturing, 2021

Controlling drum level is a major and crucial control objective in thermal power plant steam boil... more Controlling drum level is a major and crucial control objective in thermal power plant steam boilers. The drum level as a controlled variable is highly characterized by complex non-linear process dynamics as well as measurement noise and long-time delays. Developing a data-driven process model is particularly advantageous as it could be built from ongoing operational data. Such a model could be used to assist existing controllers by providing predictions regarding the drum level. The aim of this paper is to develop such a model and to propose a control architecture that can be easily integrated into existing control hardware. For that purpose, different neural networks are used, Multilayer Perceptron (MLP), Nonlinear Autoregressive Exogenous (NARX), and Long Short Term (LSTM) neural networks. LSTM and MLP were able to capture the dynamics of the process, but LSTM showed superior performance. The results demonstrate that the use of traditional machine learning criteria to evaluate a process model is not necessarily adequate. Using the model in an open-loop and a closed-loop simulation is more suitable to test its ability to capture the dynamics of the process. A novel architecture that integrates the process model within an existing closed-loop controller is proposed. The architecture uses adaptive weights to ensure that a good model is given more influence than a bad model on the controller's output.

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A Neural Network Based Motor Bearing Fault Diagnosis Algorithm and its Implementation on Programmable Logic Controller

International Journal of Intelligent Systems and Applications, 2019

This research aims to test the feasibility of Programmable Logic Controller implementation of an ... more This research aims to test the feasibility of Programmable Logic Controller implementation of an Artificial Neural Network based bearing fault diagnosis using vibration datasets. The main drawback of using a Programmable Logic Controller along with an Artificial Neural Network is that it does not support the parallel nature of neural networks. This drawback is not significant for relatively small applications like bearing diagnosis that involve very short execution time. In this paper, a three layer multilayer perceptron backpropagation neural network is trained using Levenberg-Marquardt training algorithm with vibration dataset consisting of four bearing status classes: normal, outer race way fault, inner race way fault and rolling element (ball) fault. Time-frequency domain and time domain input features were considered in this research. Both approaches have performed well during simulation phase. But the time-frequency feature extraction approach was observed to take too long scan cycle time to be implemented in real-time. This is due to the computationally intensive nature of Fast Fourier Transform algorithm involved during feature extraction. The time domain approach is proved to be feasible for Programmable Logic Controller implementation. The time domain input features used for neural network training were root mean square, variance, kurtosis and negative log likelihood values. The average performance obtained during simulation with 10-fold cross validation performance estimator was an error of 7.9 x10 -4 . The performance tests of Programmable Logic Controller implementation resulted in 100% bearing fault detection rate.

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Automatically Structuring Videos Into Audio Scenes and Audio Chapters

This paper introduces audio scenes and audio chapters in movies and presents an efficient algorit... more

A General Audio Semantic Classifier based on human perception motivated mode

Classification du signal sonore en vue d’une indexation par le contenu des documents multimédias

Les humains ont une aptitude remarquable de classifier les signaux sonores en des classes : musiq... more Les humains ont une aptitude remarquable de classifier les signaux sonores en des classes : musique, parole, applaudissement, explosion, etc. . . Face a une profusion de documents multimedias, nous proposons dans cette these de developper des machines de perception auditive automatiques dotees des capacites de classification sonore similaires a celles des humains. Nous suivons une approche inspiree par quelques etudes sur la perception humaine des classes semantiques sonores du contexte de multimedia et nous proposons ainsi un modele de memoire sonore qui est a la base d'un espace de representation du signal, nomme espace MGI (Modelisation Gaussienne Incrementale). Un comite de reseaux de neurones est couple a l'espace MGI afin de constituer un systeme de classification du signal sonore. Ce systeme a permis efficacement la classification en musique/parole, homme/femme, action/non-action, genre de musique, ainsi qu'une structuration des videos en des "scene sonores".

Method and Apparatus for Classifying Sound Signals

Highlights detection in sports videos based on audio analysis

Proceedings of the Third International Workshop on …, 2003

... A system that indexes in real time tennis videos using player and ball tracking is presented ... more

Sound recognition: a connectionist approach

This paper presents a general audio classification approach inspired by our modest knowledge abou... more This paper presents a general audio classification approach inspired by our modest knowledge about the human perception of sound. Simple psychoacoustic experiments show that the relation between short term spectral features has a great impact on the human audio classification performance. For instance, short term spectral features extracted from speech sound can be perceived as non-speech sounds if organized in a special way in time. We have developed the idea of incorporating several consecutive spectral features when modelling the audio signal in relatively long term time windows. The modelling scheme that we propose, piecewise Gaussian modelling (PGM), was combined with a neural network to develop a general audio classifier. The classifier was evaluated on the problems of speech/music classification. male/female classification and special events detection in sports videos. The good classification accuracy obtained by the classifier suggests us to continue the research in order to improve the model and to closely combine it to some well-known psychoacoustic experimental results.

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Video Scene Description: An Audio Based Approach

The increase in the amount of digital audio-visual data requires the finding of automatic methods... more The increase in the amount of digital audio-visual data requires the finding of automatic methods for content-based description of this material that facilitate the search for data and the navigation in multimedia files .While most researchers are focusing on the visual stream only for scene description, we propose using the audio stream only for automatic video scene description. In this

Mixture of experts for audio classification: an application to male female classification and musical genre recognition

In this paper we report the experimental results obtained when applying a mixture of experts to t... more In this paper we report the experimental results obtained when applying a mixture of experts to the problem of audio classification for multimedia applications. The mixture of experts is based on neural networks as individual experts and the Piecewise Gaussian Modeling was used for audio signal representation. Experimental results on two audio classification problems, male/female classification and musical genre recognition, show a clear improvement of using a mixture of experts in comparison to one individual expert.

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Audio-based description and structuring of videos

International Journal on Digital Libraries, 2006

Enabling a rapid on-the-fly view of the content of a movie requires segmenting the movie and desc... more Enabling a rapid on-the-fly view of the content of a movie requires segmenting the movie and describing the segments in a user-compatible manner. The difficulty resides in extracting relevant semantic information from the audiovisual signal, both for the segmentation and the description. We introduce in this paper audio scenes and chapters in movies and present an algorithm for automatically segmenting a video based on the audio stream only. Audio scenes and chapters are defined as the equivalent of shots and scenes in the visual domain. A tree-like audio-based structure of a video is proposed. A chapter is then classified into different chapter categories. The automatic solution to audio scene and chapter segmentation and classification is evaluated on manually segmented and classified videos

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Continuous Wavelet-Like Transform Based Music Similarity Features for Intelligent Music Navigation

Intelligent music navigation is one of the important tasks in today's music applications. In this... more Intelligent music navigation is one of the important tasks in today's music applications. In this context we propose several high-level musical similarity features that can be used in automatic music navigation, classification and recommendation. The features we propose use Continuous Wavelet-like Transform as a basic time-frequency analysis of a musical signal due to its flexibility in timefrequency resolutions. A novel 2D beat histogram is presented in the paper as a rhythmic similarity feature which is free from dependency on recording condition and does not require sophisticated adaptive algorithms of threshold finding in beat detection. This paper also describes a CWT based algorithm of multiple F0 estimation (note detection) and corresponding melodic similarity features). Evaluation of the both similarity measures is done in automatic genre classification context and playlist composition.

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A general audio classifier based on human perception motivated model

Multimedia Tools and Applications, 2007

The audio channel conveys rich clues for content-based multimedia indexing. Interesting audio ana... more The audio channel conveys rich clues for content-based multimedia indexing. Interesting audio analysis includes, besides widely known speech recognition and speaker identification problems, speech/music segmentation, speaker gender detection, special effect recognition such as gun shots or car pursuit, and so on. All these problems can be considered as an audio classification problem which needs to generate a label from low audio signal analysis. While most audio analysis techniques in the literature are problem specific, we propose in this paper a general framework for audio classification. The proposed technique uses a perceptually motivated model of the human perception of audio classes in the sense that it makes a judicious use of certain psychophysical results and relies on a neural network for classification. In order to assess the effectiveness of the proposed approach, large experiments on several audio classification problems have been carried out, including speech/music discrimination in Radio/TV programs, gender recognition on a subset of the switchboard database, highlights detection in sports videos, and musical genre recognition. The classification accuracies of the proposed technique are comparable to those obtained by problem specific techniques while offering the basis of a general approach for audio classification.

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