|
|
|
 |
Search published articles |
 |
|
Showing 3 results for Fault Detection
Mr. Hossein Safaeipour, Dr. Mehdi Forouzanfar, Dr. Amin Ramezani,
Volume 11, Issue 1 (5-2022)
Abstract
1, *2, 3
1-Ph.D. student,
*2-Assistant Professor, Department of Electrical Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran, m.forouzanfar@iauahvaz.ac.ir
3- Assistant Professor, .
In chemical closed-loop processes, interconnected industrial tanks are described by nonlinear dynamic models, sometimes under simultaneous Gaussian and non-Gaussian perturbations. Timely detection of simultaneous pipe jamming and tank leakage incipient faults is a concern of this art. In this work, a new incipient fault detection approach is proposed for application in such a system. First, the state vector is estimated using an accurate mathematical model and controller model. Then, the primary residual signal is generated using the measurements and the estimated states. Next, the generated residual signal is processed using statistical features, such as the autocorrelation test. After that, by an adaptive either fixed threshold applied in the online monitoring devised with the introduced evaluation technique, while the fault detectability is improved, the false detection problem is restricted to the system permitted number. Finally, in order to evaluate the proposed approach, the simultaneous fouling incipient fault diagnosis over the heat transfer unit built-in nonlinear closed-loop three-vessel tank system DTS200 is considered. For quantitative evaluation of the simulation results, the confusion matrix, related evaluation indices, and F1-score have been calculated.
Shahryar Shirdel, Dr. Mazdak Teimoortashloo, Dr. Mohammad Mohammadiun, Dr. Abdorreza Alavi Gharahbagh,
Volume 12, Issue 2 (9-2023)
Abstract
Due to the widespread use of electric motors in various industries, checking the conditions and diagnosing its possible faults in the early stages is one of the most important goals of intelligent industrial monitoring equipment in modern factories. Bearings are one of the mechanical parts that often fail during operation. The mechanical faults of the electric motor show themselves as vibration in the motor, which can be used to diagnose the fault, especially in the early stages. In addition, noise in industrial environments usually has less impact on vibration because vibration is extracted directly from the motor body or its base. According to this explanation, in this research, a bearing fault detection method is proposed using wavelet transform of the vibration signal in induction motors, which can detect the defect with very high accuracy. The proposed method was implemented on two different databases using Matlab 2021. The obtained results, compared with the latest articles in this field, confirmed the effectiveness of proposed method based on criteria such as accuracy and correctness. In the meantime, the advantages of proposed method, such as high speed, low calculations and its robustness to noise, were also shown.
Dr. Ehsan Akbari,
Volume 13, Issue 2 (8-2024)
Abstract
The occurrence of various faults in the transmission lines is inevitable due to the power transmission lines' complexity and length. Detecting, classifying, and locating faults in these systems can prevent further damage to the power grid. Algorithms based on the traveling wave theory are often implemented based on signal processing methods and can determine only the location of the faults. Determining the type of fault due to the wide variety of possible faults in the transmission lines can help the protection system operate more reliably and faster. Accordingly, in this paper, an integrated fault detection, classification, and location model is proposed, which uses only the voltage signals measured on one terminal of the transmission line. In order to extract features from the primary signal, Gabor wavelet transformation is used and its results will be utilized for fault detection and classification. Next, the traveling wave concept is applied to determine the fault section and estimate its location. In addition, due to the installation of the reactive power compensator in the transmission line, the performance of the protection system must be updated. Simulation results in MATLAB demonstrate the accurate performance of the proposed model in fault detection, classification, and location. By employing the proposed strategy, the average accuracy of fault classification and location is 100% and 99.573%, respectively.
|
|
