Browsing by Author "Rouani, Lahcene (supervisor)"

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Interval valued PCA-based approach for fault detection in complex systems
(2019) Louhab, Salah Eddine; Louifi, Abdelhalim; Rouani, Lahcene (supervisor)
The aim of this study is to emphasis on the detection of process sensor faults based on Principal Component Analysis (PCA). In real life case, the uncertainties of the sensor data are influencing the system and causing some difficulties in the control decision making, which in turn evokes and increases the number of false alarms and imprecise decisions. In its standard form, PCA makes no distinction between data points and the associated measurement errors which vary depending on experimental conditions. As a result, a contemporary way of representing the influence of these uncertainties on sensors has been used, namely, a representation of data in the form of interval-valued. Process modeling has been performed based on PCA for interval-valued data, where four of the most known methods have been tested. To limit the rate of false alarms, a threshold, with a certain confidence level, has been developed for both of the Hotelling’s T2, Q-statistics, and new statistics to detect the process’s faults. To confirm the ability of the proposed approach, synthetic data has been implemented, simulated, and tested on the proposed sensor fault detection. Finally, cement rotary kiln data have been tested to validate the proposed approach in reducing false alarms and missed detection rates.
Machine learning-based fault diagnosis of rotating machinery using acoustic data
(Université M'hamed Bougara Boumerdès: Institue de génie electronic et electric, 2024) Boulenache, Souhaib; Belal, Rayane; Rouani, Lahcene (supervisor)
The industrial advancement has promoted the development of machine learning based intelligent fault diagnosis methods for condition-based maintenance. Various condition-monitoring techniques can be used. However, the most reliable approaches require complex and high-cost data acquisition setups. This led to the use of acous-tic signals for fault diagnosis in this study. The study presents a machine-learning fault classificatio napproac htha tleverage sfeature sextracte dfro mth edecomposed acoustic signals using Empirical Mode Decomposition (EMD) and Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) decomposition methods. The classificatio ni sperforme dusin galgorithm sconsitin go fSuppor tVecto rMachines (SVM), K-Nearest Neighbors (KNN), Decision Trees, and Ensemble Bag. These machine-learning algorithms have been tested through different experiments to evaluate the proposed approach on two datasets, MAFAULDA Machinery Fault and the Air Compressor datasets. The results revealed that SVM exhibited superior accuracy and out performed other classifiers in most evaluation metrics. Also ,it demonstrated robustness in noisy environments, and exhibited the fastest prediction time. Decision tree demonstrated that it is the most storage-efficie ntmodel.
Simulation study of model predictive control applied to binary distillation column
(Université M'hamed Bougara Boumerdès: Institue de génie electronic et electric, 2024) Taouli, Houria Ghoufranellah; Asselah, Lamia; Rouani, Lahcene (supervisor)
Distillation columns are essential in many industries for separating liquids based on the volatility of their components. However, traditional control methods often struggle to achieve high component purity due to the process's complexity. This project investigates the use of Model Predictive Control (MPC) on a binary distillation column, comparing its performance to conventional control techniques. Guidelines for tuning the MPC controller are also provided. Simulation results show that MPC greatly improves setpoint tracking and disturbance rejection compared to traditional methods, demonstrating its potential to enhance process efficiency and product quality.