Browsing by Author "Benazzouz, D."

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Back propagation algorithm used for tuning parameters of ANN to supervise a compressor in a pharmachimical industry
(2012) Benazzouz, D.; Amrani, M.; Adjerid, Smail
This paper presents the retro-propagation algorithm for tuning the parameter of Artificial Neural Networks used by pharmachemical industry. The obtained numerical test results on lubrication and air circuits shown that the proposal improves the performance in terms of number of iterations and reliability of the models. BEKER Laboratories production line, is a Pharmaceutical production company located at Dar El Beida (Algiers-Algeria), was kept as the main target of this study. After careful inspection, the weakest and the strongest points of the system were identified and the most strategic equipment within the line (the compressor) was taken as the equipment of focus. From this specific point, failure simulations are most adequate and from this selected target, the designed system will be better positioned for failure detection during the production process. The efficiency of this approach is its fast learning, and its accuracy of detecting failure which is of the order of 10-3
Damage domains of chemically reacting industrial facilities. an adequate identification model for bhopal-like scenarios
(2011) Benikhlef, T.; Benazzouz, D.; Izquierdo, José M.; Sanchez, M.
This paper focuses on the improvement of solutions to some of the problems that arise in chemical and petrochemical risk assessment studies, namely those associated with potentially undue grouping in sequences of events, caused by different time evolutions. It provides an adequate work-horse simulation model of risky scenarios able to explore a large amount of transients at a reasonable and viable cost. It is specifically suitable for the transfer of modern dynamic reliability techniques, originated in the nuclear domain, to the chemical engineering environment. As an application, it presents results of a case study to analyse Bhopal-like scenarios. We found the model adequate to discriminate and filter out the myriad of success scenarios expected in well-protected installations, screening necessary to focus the risk studies on damage situations. © 2011 Curtin University of Technology and John Wiley & Sons, Ltd
Energy consumption modeling of H.264/AVC video decoding for GPP and DSP
(IEEE, 2013) Benmoussa, Y.; Boukhobza, J.; Senn, E.; Benazzouz, D.
Fault detection and isolation based on neural networks case study : steam turbine
(2011) Benazzouz, D.; Benammar, Samir; Adjerid, Smail
The real-time fault diagnosis system is very important for steam turbine generator set due serious fault re-sults in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using Levenberg-Marquardt algorithm related to tuning parameters of Artificial Neural Network (ANN). The model of novel fault diagnosis system by using ANN are built and analyzed. Cases of the diag-nosis are simulated. The results show that the real-time fault diagnosis system is of high accuracy and quick convergence. It is also found that this model is feasible in real-time fault diagnosis. The steam turbine is used as a power generator by SONELGAZ, an Algerian company located at Cap Djinet town in Boumerdes dis-trict. We used this turbine as our main target for the purpose of this analysis. After deep investigation, while keeping our focus on the most sensitive parts within the turbine, the weakest and the strongest points of the system were identified. Those are the points mostly adequate for failure simulations and at which the de-signed system will be better positioned for irregularities detection during the production process
GPP vs DSP : a performance/energy characterization and evaluation of video decoding
(IEEE, 2013) Benmoussa, Y.; Boukhobza, J.; Senn, E.; Benazzouz, D.
Health Monitoring Approach of Bearing : Application of Adaptive Neuro Fuzzy Inference System (ANFIS) for RUL-Estimation and Autogram Analysis for Fault-Localization
(Institute of Electrical and Electronics Engineers, 2020) Gougam, Fawzi; Rahmoune, C.; Benazzouz, D.; Varnier, C.; Nicod, J.-M.
Bearings usually operate under harsh conditions which result in a dynamic behavior generating non-stationary vibration signals and overwhelmed by noise. Therefore, bearing fault diagnosis and prognosis become difficult since the purpose is to extract robust features able to detect the appearance of faults, monitoring the degradation of health state and to predict the remaining useful life (RUL) of bearing. The aim of this paper, is to propose a method for bearing faults feature-extraction using adaptive neuro fuzzy inference system (ANFIS) and autogram analysis. First, times domain features are applied for the raw vibration signal. Then, the selected features are computed to will be analyzed as one of the characteristics that describes the degradation of state system. After that, the curve fitting (smoothing) is applied to normalize the amplitude of the irregular values relatively to others feature values. The calculated value of acquired signal cannot be smoothed or calculated three or more times, hence ANFIS intervenes for modeling the transfer from an indeterminate input to a more relevant value for monitoring the fault evolution. Then, the output of ANFIS estimates the days of acquisition and predict the RUL of bearing. Finally, the autogram analysis is used to identify the degraded element in the bearing
Monitoring gear fault by using motor current signature analysis and fast kurtogram method
(2013) Rahmoune, C.; Benazzouz, D.
New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network
(SAGE Publications Inc., 2020) Afia, Adel; Rahmoune, C.; Benazzouz, D.; Merainani, B.; Fedala, S.
Nowadays, fault detection, identification, and classification seem to be the most difficult challenge for gear systems. It is a complex procedure because the defects affecting gears have the same frequency signature. Thus, the variation in load and speed of the rotating machine will, inevitably, lead to erroneous detection results. Moreover, it is important to discern the nature of the anomaly because each gear defect has several consequences on the mechanism’s performance. In this article, a new intelligent fault diagnosis approach consisting of Autogram combined with radial basis function neural network is proposed. Autogram is a new sophisticated enhancement of the conventional Kurtogram, while radial basis function is used for classification purposes of the gear state. According to this approach, the data signal is decomposed by maximal overlap discrete wavelet packet transform into frequency bands and central frequencies called nodes. Thereafter, the unbiased autocorrelation of the squared envelope for each node is computed in order to calculate the kurtosis for each one at every decomposition level. Finally, the feature matrix obtained from the previous step will be the input of the radial basis function neural network to provide a new automatic gear fault diagnosis technique. Experimental results from the gearbox with healthy state and five different types of gear defects under variable speeds and loads indicate that the proposed method can successfully detect, identify, and classify the gear faults in all cases
A new method based on fast kurtogram for the identification of pitting fault versus crack fault in gearbox systems
(Vibromechanika, 2014) Belalouache, K.; Benazzouz, D.; Rahmoune, C.
New method for gear fault diagnosis using empirical wavelet transform, Hilbert transform, and cosine similarity metric
(SAGE Publications, 2020) Bettahar, Toufik; Rahmoune, C.; Benazzouz, D.; Merainani, B.
In this article, a new feature extraction method is proposed for gear fault diagnosis by combining the empirical wavelet transform, Hilbert transform, and cosine similarity metric. In the first place, a number of empirical mode components acquisitions are done, using empirical wavelet transform. Since different empirical modes have different sensitivities to fault, not all of them are needed for further analysis. Therefore, the most sensitive empirical modes are selected using the cosine similarity metric method. Hilbert transform was then used to obtain the envelope for amplitude modulation. Finally, spectral analysis using fast Fourier transform is applied on the obtained envelope. Gear test rig with gears under different fault states has revealed an effective outcome and a solid stability of this new approach. The obtained results show that our approach is efficiently able to detect and expose the gear faults signatures, that is, it highlights their frequencies and the corresponding harmonics with respect to the rotary frequency. Furthermore, this proposed method demonstrates more satisfactory and advantageous performances compared to those of fast kurtogram, or the autogram
On the energy efficiency of parallel multi-core vs hardware accelerated HD video decoding
(CEUR-WS, 2014) Benmoussa, Y.; Boukhobza, J.; Senn, E.; Benazzouz, D.
Performance analysis and optimization of molten a salt cavity receiver in solar power plants
(2021) Rouibah, A.; Benazzouz, D.; Rahmani, K.; Benmessaoud, T.
The objective of this paper focuses on optimizing the performance of solar systems. These systems play an essential role in the production of electricity worldwide. They are sources of clean energy which can be exploited in areas rich with solar potential.In this paper, the goal is to optimize the heat flow absorbed by the receiver. To do this, genetic algorithms are proposed as an approach able of solving the problematic subject with constraints that are countable and interlinked. These constraints are inspired from the energy balance of the solar tower concentrator under study.The results obtained by numerical analysis based on these constraints and the objective functions (maximizing the heat flow received and minimizing the losses of the heat flow) shows the existence of an optimal receiver efficiency value for the heliostat surface total, the receiver temperature, the molten salt temperature, the receiver opening surface, the receiver surface, the diameter, the thickness, the tubes thermal conductivity of the receiver and the steam flow at turbine inlet. In addition, the energy efficiency of the solar tower system improves better depending on the power cycle chosen such as the Hirn cycle with reheating and racking used in our case
Performance of an unbuffered Beta model using stochastic Petri nets
(International Journal of Modelling and Simulation, 1999) Benazzouz, D.; Farah, A.
Many multistage interconnection networks (MINs) and single stage interconnection networks (SSINs) have been proposed for parallel computer systems and for fast packet switching in high speed new works. The cost, performance, and fault-tolerance capability of the interconnection networks (INs) becomes very important in the design considerations of a multiprocessor systems. Several types of INs have been proposed, notably multistage and single-stage interconnection networks. There have been extensive studies on MIN (e.g., performance analysis, methods to improve the throughput, priority, etc.), but relatively little work on SSINs has appeared in the literature. In this paper we evaluate the unbuffered Beta topology of SSIN using stochastic Petri nets. We present an approximate analytical model. We analyze the random delay experienced by a message traversing the network for uniform traffic. Messages can have different sizes. Each sender can accept one packet per cycle and route it to the appropriate receiver. It is shown that the bandwidth increases when the data transfer increases. In addition, it is shown that the average transfer time increases slowly compared to the increase of processors. The power of this model is that, firstly, it presents an acceptable number of states, and secondly, the model can be easily generalized