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Subject: search.filters.subject.Fault detection

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    A data driven fault diagnosis approach for robotic cutting tools in smart manufacturing
    (International Society of Automation, 2025) Afia, Adel; Gougam, Fawzi; Soualhi, Abdenour; Wadi, Mohammed; Tahi, Mohamed; Tahi, Mohamed
    In smart manufacturing within Industry 4.0, tool condition monitoring (TCM) is used to improve productivity and machine availability by leveraging advanced sensors and computational intelligence to prevent tool damage. This paper develops a hybrid methodology using heterogeneous sensor measurements for monitoring robotic cutting tools with four tool states: healthy, surface damage, flake damage and broken tooth. The proposed approach integrates the maximal overlap discrete wavelet packet transform (MODWPT) with health indicators to construct feature matrices for each tool state. Feature selection is performed using the tree growth algorithm (TGA) to reduce computation time and improve feature space separation by selecting only relevant features. The selected features are input into a Gaussian mixture model (GMM) to detect, identify and classify each tool state with high accuracy. The proposed method provides a classification accuracy of 99.04 % for vibration, 95.51 % for torque, and 91.67 % for force signals. Using unseen vibration data, the model achieved a test accuracy of 98.44 %, demonstrating a high degree of generalizability. Comparative analysis demonstrates that our proposed approach provides superior feature discrimination and model stability, balancing computational efficiency and classification accuracy, validating the TGA-GMM framework as an effective solution for tool fault diagnosis in noisy, high-dimensional data.
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    Enhancing Fault Detection in Stochastic Environments Using Interval-Valued KPCA: A Cement Rotary Kiln Case Study
    (Institute of Electrical and Electronics, 2025) Louifi, Abdelhalim; Kouadri, Abdelmalek; Harkat, Mohamed-Faouzi; Bensmail, Abderazak; Mansouri, Majdi
    Fault detection in industrial processes is challenging due to significant data uncertainty, which complicates the accurate modeling of interval-valued data and the quantification of errors necessary for reliable detection. Existing approaches, such as kernel principal component analysis (KPCA), struggle with these challenges because they rely on single-valued data representations and are unable to effectively handle interval-based variability. To address these limitations, this paper introduces the interval-valued model KPCA (IV-KPCA), which extends KPCA by redefining similarity measures and kernel functions to accommodate interval-valued uncertainty. IV-KPCA preserves the interval structure throughout the modeling process, enhancing robustness to dynamic uncertainties and improving fault detection in complex nonlinear systems. Within this framework, fault detection statistics (T 2 , Q, and 8) are developed to enable precise error quantification. The proposed method is validated on a cement rotary kiln process, a highly stochastic industrial system characterized by significant uncertainties. Experimental results demonstrate that IV-KPCA reduces false alarms, missed detections, and detection delays by over 100%, 90%, and 95%, respectively, compared to traditional methods. These findings underscore the potential of IV-KPCA in enhancing fault detection performance in complex, uncertain environments
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    Sensor Fault Detection in Uncertain Large-Scale Systems Using Interval-Valued PCA Technique
    (IEEE, 2025) Louifi, Abdelhalim; Kouadri, Abdelmalek; Harkat, Mohamed-Faouzi
    Principal component analysis (PCA)-based fault detection and diagnosis (FDD) is a well-established, data- driven method that has shown remarkable performance. Despite the excellent reputation of the PCA, it is not an opti- mal solution, mainly due to the effect of system parameters’ uncertainties and imprecise measurements. These drasti- cally affect the decision-making concerning the operating state of the process. In this article, the data collected by different sensors are transformed from a single value to an interval value form by which errors and uncertainties in the measurements are quantified satisfactorily. Then, the process modeling based on the PCA technique has been duly performed for interval-valued. Afterward, the well-known fault detection statistics T 2 , Q, and 8 are obtained under an interval-valued representation. The developed technique is tested in the cement rotary kiln process. Its performance in terms of false and missed alarms and detection delay is compared with that of other techniques through an actual involuntary system fault and other different types of sensor faults. The obtained results show high superiority in detecting accurately and quickly distinct faults in a stochastic environment, including unknown and uncontrolled uncertainties. Consequently, the results have been reduced by more than 33%, 85%, and 45% for T 2 , Q, and 8, respectively, compared with the best results of the studied methods.
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    Improving kernel PCA-based algorithm for fault detection in nonlinear industrial process through fractal dimension
    (Institution of Chemical Engineers, 2023) Kaib, Mohammed Tahar Habib; Kouadri, Abdelmalek; Harkat, Mohamed Faouzi; Bensmail, Abderazak; Mansouri, Majdi
    Principal Component Analysis (PCA) is a widely used technique for fault detection and diagnosis. PCA works well when the data set has linear characteristics. However, most industrial processes have nonlinear characteristics in their data. Kernel PCA (KPCA) is an alternative solution for such types of data sets. This solution doesn’t come without a cost since one of KPCA’s disadvantages is a large number of observations which results in more occupied storage space and more execution time than the PCA technique. Furthermore, if the data is too large it may minimize the monitoring performance of the KPCA model. Reduced KPCA (RKPCA) is a solution for the conventional KPCA limitations. Firstly, RKPCA can deal with nonlinear characteristics without crucial problems because it is based on the KPCA algorithm with a data reduction part where it keeps most of the data’s infor- mation. Thus, by reducing the number of observations RKPCA reduces the occupied storage space and execution time while preserving tolerable monitoring performance. The proposed RKPCA algorithm consists of two parts. First, the large-sized training data set is reduced using the fractal dimension technique (correlation dimension). Afterward, the KPCA model is developed through the obtained reduced training data set. The proposed scheme is applied to the Tennessee Eastman Process and the Cement Plant Rotary Kiln data sets to evaluate its performance in comparison with other algorithms.
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    Enhancing fault diagnosis of undesirable events in oil & gas systems: A machine learning approach with new criteria for stability analysis and classification accuracy
    (SAGE, 2023) Sahraoui, Mohammed Amine; Rahmoune, Chemseddine; Zair, Mohamed; Gougam, Fawzi; Damou, Ali
    Petroleum serves as a cornerstone of global energy supply, underpinning economic development. Consequently, the effective detection of faults in oil and gas (O&G) wells is of paramount importance. In response to the limitations observed in prior research, this study presents an innovative fault diagnosis system, rooted in machine learning techniques. Our approach encompasses a comprehensive analysis, incorporating stability assessment via standard deviation (STD), and a meticulous evaluation of accuracy and stability for distinct fault scenarios. By integrating data preprocessing, feature selection methods, and deploying a robust random forest classifier, our model achieves a substantial enhancement in fault classification accuracy and stability. Extensive experimentation substantiates the superiority of our approach, surpassing the performance of previous studies that predominantly emphasized overall accuracy while disregarding stability analysis. Notably, our model attains remarkable accuracies, notably achieving a flawless 100% accuracy for scenario 3 faults. Detailed examination of mean accuracies and STDs further reinforces the precision and consistency of our model's predictive capabilities. Additionally, a qualitative assessment underscores the practical utility and reliability of our model in accurately identifying critical fault types. This research significantly advances fault detection methodologies within the O&G industry, providing valuable insights for decision-making systems in oil well operations.
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    RKPCA-based approach for fault detection in large scale systems using variogram method
    (Elsevier, 2022) Kaib, Mohammed Tahar Habib; Kouadri, Abdelmalek; Harkat, Mohamed Faouzi; Bensmail, Abderazak
    Principal Component Analysis (PCA)-based approach for fault detection is a simple and accurate data-driven technique for feature extraction and selection. However, PCA performs poorly if the data used has nonlinear characteristics where this type of data is widely present in most industrial processes. To overcome this drawback, Kernel PCA (KPCA) is an alternative technique used to work on this type of data but it requires more computation time and memory storage space for large-sized data sets. Many size reduction techniques have been developed to select the most relevant observations that will be employed by KPCA. This, known as Reduced KPCA (RKPCA), consequently requires less computation time and memory storage space than KPCA. Besides, it possesses the advantages of both KPCA and standard PCA. In this paper, a reduction in the size of a data set based on a multivariate variogram is proposed. According to its conventional formalism, the uncorrelated observations are selected and kept to form a reduced training data set. Afterward, the KPCA model is built through this data set for faults detection purposes. The proposed RKPCA scheme is tested using an actual involuntary process fault and various simulated sensor faults in a cement plant. Compared to other RKPCA techniques, the developed one yields better results
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    New reduced kernel PCA for fault detection and diagnosis in cement rotary kiln
    (Elsevier, 2020) Bencheikh, Fares; Harkat, M. F.; Kouadri, A.; Bensmail, A.
    Fault detection and diagnosis (FDD) based on data-driven techniques play a crucial role in industrial process monitoring. It intends to promptly detect and identify abnormalities and enhance the reliability and safety of the processes. Kernel Principal Component Analysis (KPCA) is a powerful FDD based data-driven method. It has gained much interest due to its ability in monitoring nonlinear systems. However, KPCA suffers from high computing time and large storage space when a large-sized training dataset is used. So, extracting and selecting the more relevant observations could provide a good solution to high computation time and memory re- quirements costs. In this paper, a new Reduced KPCA (RKPCA) approach is developed to address that issue. It aims to preserve one representative observation for each similar and selected Euclidean distance between training samples. Afterwards, the obtained reduced training dataset is used to build a KPCA model for FDD purposes. The developed RKPCA scheme is tested and evaluated across a numerical example and an actual involuntary process fault and various simulated sensor faults in a cement plant. The obtained results show high monitoring perfor- mance with highest robustness to false alarms and maximum fault detection sensitivity compared to conventional PCA, KPCA and other well-established RKPCA techniques. Furthermore, the unified contribution plot method demonstrates superior potentials in identifying faulty variables.
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    Bearing fault detection under time-varying speed based on empirical wavelet transform, cultural clan-based optimization algorithm, and random forest classifier
    (SAGE Publications, 2021) Moussaoui, Imane; Rahmoune, Chemseddine; Zair, Mohamed; Benazzouz, Djamel
    Bearings are massively utilized in industries of nowadays due to their huge importance. Nevertheless, their defects can heavily affect the machines performance. Therefore, many researchers are working on bearing fault detection and classification; however, most of the works are carried out under constant speed conditions, while bearings usually operate under varying speed conditions making the task more challenging. In this paper, we propose a new method for bearing condition monitoring under time-varying speed that is able to detect the fault efficiently from the vibration signatures. First, the vibration signal is processed with the Empirical Wavelet Transform to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then, the features’ set is reduced using the Cultural Clan-based optimization algorithm by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm “Random Forest” is used to train a model able to classify the fault based on the selected features. The proposed method was tested on a time-varying real dataset consisting of three different bearing health states: healthy, outer race defect, and inner race defect. The obtained results indicate the ability of our proposed method to handle the speed variability issue in bearing fault detection with high efficiency
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    Shading fault detection in a grid-connected PV system using vertices principal component analysis
    (Elsevier, 2021) Rouani, Lahcene; Harkat, Mohamed Faouzi; Kouadri, Abdelmalek; Mekhilef, Saad
    Partial shading severely impacts the performance of the photovoltaic (PV) system by causing power losses and creating hotspots across the shaded cells or modules. Proper detection of shading faults serves not only in harvesting the desired power from the PV system, which helps to make solar power a reliable renewable source, but also helps promote solar versus other fossil fuel electricity-generation options that prevent making climate change targets (e.g. 2015’s Paris Agreement) achievable. This work focuses primarily on detecting partial shading faults using the vertices principal component analysis (VPCA), a data-driven method that combines the simplicity of its linear model and the ability to consider the uncertainties of the different measurements of a PV system in an interval format. Data from a gridconnected monocrystalline PV array, installed on the rooftop of the Power Electronics and Renewable Energy Research Laboratory (PEARL), University of Malaya, Malaysia, have been used to train the VPCA model. To prove the effectiveness of this VPCA method, four partial shading patterns have been created. The obtained performance has, then, been tested against a regular PCA. In addition to its ability to acknowledge the uncertainty of a PV system, the VPCA method has shown an enhanced performance of detecting partial shading fault in comparison with the standard PCA. Also, included in the article is an extension of the contribution plot diagnosis-based method, of the Q-statistic, to the interval-valued case aiming to pinpoint the out-of-control variables.
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    Gearboxes fault detection under operation varying condition based on MODWPT, Ant colony optimization algorithm and Random Forest classifier
    (SAGE Publications, 2021) Ikhlef, Boualem; Rahmoune, Chemseddine; Bettahar, Toufik; Benazzouz, Djamel
    Gearboxes are massively utilized in nowadays industries due to their huge importance in power transmission; hence, their defects can heavily affect the machines performance. Therefore, many researchers are working on gearboxes fault detection and classification. However, most of the works are carried out under constant speed conditions, while gears usually operate under varying speed and torque conditions, making the task more challenging. In this paper, we propose a new method for gearboxes condition monitoring that is efficiently able to reveal the fault from the vibration signatures under varying operating condition. First, the vibration signal is processed with the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then the features set are reduced using the Ant colony optimization algorithm (ACO) by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm Random Forest (RF) is used to train a model able to classify the fault based on the selected features. The innovative aspect about this method is that, unlike other existing methods, ACO is able to optimize not only the features but also the parameters of the classifier in order to obtain the highest classification accuracy. The proposed method was tested on varying operating condition real dataset consisting of six different gearboxes. In the aim to prove the performance of our method, it had been compared to other conventional methods. The obtained results indicate its robustness, and its accuracy stability to handle the varying operating condition issue in gearboxes fault detection and classification with high efficiency