Author
Abdulrahman Youssef
Other affiliations: Université Paris-Saclay, Supélec
Bio: Abdulrahman Youssef is an academic researcher from CentraleSupélec. The author has contributed to research in topics: Fault detection and isolation & Fault (power engineering). The author has an hindex of 5, co-authored 6 publications receiving 118 citations. Previous affiliations of Abdulrahman Youssef include Université Paris-Saclay & Supélec.
Papers
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TL;DR: An incipient fault detection method that does not need any a priori information on the signals distribution or the changed parameters is proposed and an analytical model of the fault detection performances (False Alarm Probability and Missed Detection Probability) is developed.
99 citations
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TL;DR: In this article, a fault detection, isolation and estimation method based on data-driven approach was developed for feature extraction and feature analysis using statistical techniques, where the Principal Component Analysis (PCA) method was used to extract the features and to reduce the data dimension.
34 citations
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01 Sep 2014
TL;DR: An analytical model of the fault detection performances (False Alarm Probability and Miss Detection Probability) based on the KLD including the noisy environment characteristics is developed and an optimization procedure is applied to set the optimal fault detection threshold depending on the SNR and the fault severity.
Abstract: Sensible and reliable incipient fault detection methods are major concerns in industrial processes. The Kullback Leibler Divergence (KLD) has proven to be particularly efficient. However, the performance of the technique is highly dependent on the detection threshold and the Signal to Noise Ratio (SNR). In this paper, we develop an analytical model of the fault detection performances (False Alarm Probability and Miss Detection Probability) based on the KLD including the noisy environment characteristics. Thanks to this model, an optimization procedure is applied to set the optimal fault detection threshold depending on the SNR and the fault severity.
9 citations
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01 Nov 2013TL;DR: This paper is an attempt to determine an analytical model allowing to estimate a theoretical threshold for fault detection based on the Fault to Noise Ratio (FNR), developed using the Kullback-Leibler Divergence (KLD).
Abstract: Process-history based methods are very commonly used for fault diagnosis and detection. However their efficiency is closely related to the quality of the measured data. In noisy environments, they usually fail particularly for incipient faults. This paper is an attempt to determine an analytical model allowing to estimate a theoretical threshold for fault detection based on the Fault to Noise Ratio (FNR). This model is developed using the Kullback-Leibler Divergence (KLD). For feature extraction, the used data are previously processed through Principal Component Analysis (PCA). The model is validated with simulated data and the results are so far very encouraging.
8 citations
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01 Aug 2015
TL;DR: An analytical model of the Kullback-Leibler Divergence for Gamma distributed data to be used for the fault severity estimation and shows that for incipient faults (<;10%) in usual noise conditions (SNR>40dB), the analytical model is accurate enough with a relative error around 10%.
Abstract: Incipient fault diagnosis has become a key issue for reliability and safety of industrial processes. Data-driven methods are effective for feature extraction and feature analysis using multivariate statistical techniques. Beside fault detection, fault estimation is essential for making the appropriate decision (safe stop or fault accommodation). Therefore, in this paper, we have developed an analytical model of the Kullback-Leibler Divergence (KLD) for Gamma distributed data to be used for the fault severity estimation. In the Principal Component Analysis (PCA) framework, the proposed model of the KLD has been analysed and compared to an estimated value of the KLD using the Monte-Carlo estimator. The results show that for incipient faults ( 40dB), the analytical model is accurate enough with a relative error around 10%.
7 citations
Cited by
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TL;DR: An effective and reliable deep learning method known as stacked denoising autoencoder (SDA), which is shown to be suitable for certain health state identifications for signals containing ambient noise and working condition fluctuations, is investigated.
591 citations
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01 Jan 2006304 citations
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TL;DR: The proposed domain adaptation method offers a new and promising tool for intelligent fault diagnosis and can be efficiently extracted in this way, and the cross-domain testing performance can be significantly improved.
283 citations
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TL;DR: The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach, and significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length.
Abstract: Effective fault diagnosis of rotating machinery has always been an important issue in real industries. In the recent years, data-driven fault diagnosis methods such as neural networks have been receiving increasing attention due to their great merits of high diagnosis accuracy and easy implementation. However, it is mostly difficult to fully train a deep neural network since gradients in optimization may vanish or explode during back-propagation, which results in deterioration and noticeable variance in model performance. In fault diagnosis researches, larger data sequence of machinery vibration signal containing sufficient information is usually preferred and consequently, deep models with large capacity are generally adopted. In order to improve network training, a residual learning algorithm is proposed in this paper. The proposed architecture significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length. Little prior expertise on fault diagnosis and signal processing is required, that facilitates industrial applications of the proposed method. Experiments on a popular rolling bearing dataset are implemented to validate the proposed method. The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach.
239 citations
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TL;DR: Attention mechanism is introduced to assist the deep network to locate the informative data segments, extract the discriminative features of inputs, and visualize the learned diagnosis knowledge.
221 citations