Omid Rahmani Seryasat

Bio: Omid Rahmani Seryasat is an academic researcher from K.N.Toosi University of Technology. The author has contributed to research in topics: Ball bearing & Electrical equipment. The author has an hindex of 7, co-authored 18 publications receiving 158 citations. Previous affiliations of Omid Rahmani Seryasat include Hakim Sabzevari University.

Intelligent fault detection of electrical equipment in ground substations using thermo vision technique

Abstract: The aim of this paper is to detect the electrical equipment faults by making use of the moment method and statistical features of thermo images. Using the support vector machine (SVM) as a classifier and Zernike moment as image feature, a method for the intelligent detection of electrical equipment faults based on thermography has been introduced in this paper. By attention to the commonly occurring faults in the substations of distribution networks, two major faults occurring in ground substations low pressure panels that are related to the fuses have been chosen. The simulation results have been applied to the completely practical databases of real images of the distribution networks of North West of Tehran

Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy mean and root mean square (RMS)

Abstract: According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method using FFT and wavelet energy entropy mean and root mean square (RMS), energy entropy mean is put forward. in this paper, Firstly, original rushing vibration signals is transformed into a frequency domain, and is comminuted wavelet components, then the theory of energy entropy mean and root mean square is proposed. The analysis results from energy entropy and root mean square of different vibration signals show that the energy and root mean square of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to diagnose ball bearing faults, we run the test rig with faulty ball bearing in various speeds and loads and collect vibration signals in each run then, calculate the energy entropy mean and root mean square which indicate the fault types. The analysis results from ball bearing signals with six different faults in various working conditions show that the diagnosis approach based on using wavelet and FFT to extract the energy and root mean square of different frequency bands can identify ball bearing faults accurately and effectively. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis.

Multi-fault diagnosis of ball bearing based on features extracted from time-domain and multi-class support vector machine(MSVM)

Abstract: Due to the importance of rolling bearings as one of the most populous used industrial machinery elements, development of proper monitoring and fault diagnosis procedure to suppression malfunctioning and failure of these elements during operation is necessary. For rolling bearing fault detection, it is expected that a desired time domain analysis method has good computational efficiency. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with extracting features in time-domain from the vibration signals and multi-class support vector machine (MSVM) that used to the detection and classification of rolling-element bearing faults. The roller bearings nature of vibration reveals its condition and the features that show the nature are to be extracted through some indirect means. The method consists of two stages. Firstly, the features in time-domain from the vibration signals, which are widely used in fault diagnostics, are extracted. Finally, the features that extracted are classified successfully using MSVM classifier and the work condition and fault patterns of the roller bearings and then faults are diagnosis real tine based on Voting.

Segmenting breast cancerous regions in thermal images using fuzzy active contours

Abstract: Breast cancer is the main cause of death among young women in developing countries. The human body temperature carries critical medical information related to the overall body status. Abnormal rise in total and regional body temperature is a natural symptom in diagnosing many diseases. Thermal imaging (Thermography) utilizes infrared beams which are fast, non-invasive, and non-contact and the output created images by this technique are flexible and useful to monitor the temperature of the human body. In some clinical studies and biopsy tests, it is necessary for the clinician to know the extent of the cancerous area. In such cases, the thermal image is very useful. In the same line, to detect the cancerous tissue core, thermal imaging is beneficial. This paper presents a fully automated approach to detect the thermal edge and core of the cancerous area in thermography images. In order to evaluate the proposed method, 60 patients with an average age of 44/9 were chosen. These cases were suspected of breast tissue disease. These patients referred to Tehran Imam Khomeini Imaging Center. Clinical examinations such as ultrasound, biopsy, questionnaire, and eventually thermography were done precisely on these individuals. Finally, the proposed model is applied for segmenting the proved abnormal area in thermal images. The proposed model is based on a fuzzy active contour designed by fuzzy logic. The presented method can segment cancerous tissue areas from its borders in thermal images of the breast area. In order to evaluate the proposed algorithm, Hausdorff and mean distance between manual and automatic method were used. Estimation of distance was conducted to accurately separate the thermal core and edge. Hausdorff distance between the proposed and the manual method for thermal core and edge was 0.4719 ± 0.4389, 0.3171 ± 0.1056 mm respectively, and the average distance between the proposed and the manual method for core and thermal edge was 0.0845 ± 0.0619, 0.0710 ± 0.0381 mm respectively. Furthermore, the sensitivity in recognizing the thermal pattern in breast tissue masses is 85 % and its accuracy is 91.98 %.A thermal imaging system has been proposed that is able to recognize abnormal breast tissue masses. This system utilizes fuzzy active contours to extract the abnormal regions automatically.

Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey

Abstract: With the tremendous revival of artificial intelligence, predictive maintenance (PdM) based on data-driven methods has become the most effective solution to address smart manufacturing and industrial big data, especially for performing health perception (e.g., fault diagnosis and remaining life assessment). Moreover, because the existing PdM research is still in primary experimental stage, most works are conducted utilizing several open-datasets, and the combination with specific applications such as rotating machinery is especially rare. Hence, in this paper, we focus on data-driven methods for PdM, present a comprehensive survey on its applications, and attempt to provide graduate students, companies, and institutions with the preliminary understanding of the existing works recently published. Specifically, we first briefly introduce the PdM approach, illustrate our PdM scheme for automatic washing equipment , and demonstrate the challenges encountered when we conduct a PdM research. Second, we classify the specific industrial applications based on six algorithms of machine learning and deep learning (DL), and compare five performance metrics for each classification. Furthermore, the accuracy (a metric to evaluate the algorithm performance) of these PdM applications is analyzed in detail. There are some important conclusions: 1) the data used in the summarized literature are mostly from public datasets, such as case western reserve university (CWRU)/intelligent maintenance systems (IMS); and 2) in recent years, researchers seem to focus more on DL algorithms for PdM research. Finally, we summarize the common features regarding our surveyed PdM applications and discuss several potential directions.

Prognosis of Bearing Failures Using Hidden Markov Models and the Adaptive Neuro-Fuzzy Inference System

Abstract: Prognostics and health management (PHM) play a key role in increasing the reliability and safety of systems especially in key sectors (military, aeronautical, aerospace, nuclear, etc.). This paper presents a new methodology which combines data-driven and experience-based approaches for the PHM of roller bearings. The proposed methodology uses time domain features extracted from vibration signals as health indicators. The degradation states in bearings are detected by an unsupervised classification technique called artificial ant clustering. The imminence of the next degradation state in bearings is given by hidden Markov models, and the estimation of the remaining time before the next degradation state is given by the multistep time series prediction and the adaptive neuro-fuzzy inference system. A set of experimental data collected from bearing failures is used to validate the proposed methodology. Experimental results show that the use of data-driven and experience-based approaches is a suitable strategy to improve the PHM of roller bearings.