Sharif Digital Repository

Detection of rolling bearing faults by vibration analysis is an important part of condition monitoring programs. In this paper a new method for detection of bearing defects based on a new concept of local surface roughness, is proposed. When a defect in the bearing grows then roughness of the defective surface increases and measurement of the roughness can be a good indicator of the bearing defect. In this paper a method of indirectly measuring surface roughness by using vibration signal is introduced. Several attached examples including both numerically simulated signals and actual experimental data show the effectiveness of the new, easy-to-implement method  

This paper gives the final Solution for vibration reduction in a centrifugal pump. Vibration measurement in different conditions has been carried out in order to find the main reason for excessive vibration of the pumps. In the first stage several parameters including cavitation, not working in the pump design condition and mechanical and electrical faults assumed to be the reason for the pump vibration. By vibration analysis it is found that the major reason for the pump vibration is working in off design conditions. More over dissolved air in the suction fluid can possibly cause two-phase flow leading to the pump vibration. For solving both problems considering pump performance curves it... 

Process Fault Diagnosis (PFD) involves interpreting the current status of the plant given sensor readings and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for PFD. Neural networks have been used to solve PFD problems in chemical processes, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks (HANN) in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks (SANN). The lower efficiency of HANN, in comparison to SANN, in PFD is elaborated and analyzed. Also, the concept... 

Transfer function (TF) is an acknowledged method for power transformer mechanical faults detection. However the past published works mostly discovered how to specify the faults levels and paid less attention to detection of the type of faults using comparison of TFs. whereas, it seems important for most of the applications to specify the type of fault without opening the unit. This paper presents a new method based on vector fitting (VF) to compare the TFs and specify the type, level and location of the fault. For development of the method, and its verification the required measurements are carried out on four model transformers; under intact condition, and under different fault conditions... 

In this paper, the fuzzy system has been used for fault detection and diagnosis of a yeast fermentation bioreactor based on measurements corrupted by noise. In one case, parameters of membership functions are selected in a conventional manner. In another case, using certainty factors between normal and faulty conditions the optimal values of these parameters have been obtained through the genetic algorithm. These two cases are compared based on their performances in fault diagnosis of a yeast fermentation bioreactor for three different conditions. The simulation results indicate that the fuzzy-genetic system is superior in multiple fault detection for the conditions where the minimum and... 

In this paper, an algorithm for fault diagnosis of permanent magnet DC electromotors.has been investigated, based on vibration and electrical current monitoring. Several permanent magnet DC electromotors.with previously determined faults have been prepared and the vibration, current and speed data have been measured. The relationship between certain related measured data and faults has been determined. A fault diagnosis algorithm has been developed in this research based on these relationships. This algorithm can be used in mass production lines for quality control  

Most multivariate quality control procedures evaluate the in-control or out-of-control condition based upon an overall statistic, like Hotelling's T2. Although T2 is optimal for finding a general shift in mean vectors, it is not optimal for shifts that occur for some subset of variables. This introduces a persistent problem in multivariate control charts, namely the interpretation of a signal that often discourages practitioners in applying them. In this paper, we propose an artificial neural network based model to diagnose faults in out-of-control conditions and to help identify aberrant variables when Shewhart-type multivariate control charts based on Hotelling's T2 are used. The results... 

Lumbar disc diseases are the commonest complaint of Lower Back Pain (LBP). In this paper, a new method for automatic diagnosis of lumbar disc herniation is proposed which is based on clinical Magnetic Resonance Images (MRI) data. We use T2-W sagittal and myelograph images. Our method uses Otsu thresholding method to extract the spinal cord from MR images of Lumbar disc. In the next step, a third-order polynomial is aligned on the extracted spinal cords, and in the end of preprocessing step all the T2-W sagittal images are prepared for extracting disc boundary and labeling. After labeling and extracting a ROI for each disc, intensity and shape features are used for classification. The... 

A new computer-aided diagnosis (CAD) system for detecting malignant melanoma from dermoscopy images based on a fixed grid wavelet network (FGWN) is proposed. This novel approach is unique in at least three ways: (i) the FGWN is a fixed WN which does not require gradient-type algorithms for its construction, (ii) the construction of FGWN is based on a new regressor selection technique: D-optimality orthogonal matching pursuit (DOOMP), and (iii) the entire CAD system relies on the proposed FGWN. These characteristics enhance the integrity and reliability of the results obtained from different stages of automatic melanoma diagnosis. The DOOMP algorithm optimises the network model approximation... 

Acute lymphoblastic leukemia is one of the most common hematologic malignancies among children, caused by uncontrolled growth of leukocytes. Since the main hallmarks of the disease is not specific, a considerable number of patients have been being misdiagnosed. Early diagnosis of the disease is usually made by morphological investigation of leukocytes under microscope. In light of the facts that decrease in cytoplasm-to-nucleus ratio is one of the main indicators of cancerous cells, and an accurate segmentation phase will lead to extraction of representative features, segmentation step is acknowledged as being crucial in design of a computer aided diagnosis (CAD). Previous researches have... 

Since Gram-negative bacteria have a predominant role in nosocomial infections, there are high demands to develop a fast and sensitive method for diagnosis of bacteria in clinical samples. To address this challenge, we designed a novel electrochemical biosensor based on aptamers immobilized in engineered zeolitic imidazolate Framework-8 (ZIFs-8) via EDC-NHS chemistry. Cyclic voltammetry and electrochemical impedance spectroscopy techniques were conducted to monitor the electrochemical characterization. With respect to unique π-πinteractions between aptamer and graphene oxide (GO), the differential pulse voltammetry technique was applied with ferrocene-graphene oxide (Fc-GO) as an... 

During the last decade, reports show that the incidence and prevalence of Alzheimer's disease (AD) and other dementias have significantly increased. AD poses an enormous escalating threat to health services and resources. Early diagnosis of AD is recognized as one of the major challenges and primary aims in scientific communities. With the arrival of nanoscience and nanotechnology to medicine, hopes for early diagnosis and treatment of AD have considerably increased. To this end, nanobioresearchers are focused on three major areas consisting of early detection and recognition, biological markers and diagnosis, and pharmacotherapy. Several efforts are in progress for the discovery of new... 

Design and development of fault diagnosis schemes (FDS) for electric power distribution systems are major steps in realizing the self-healing function of a smart distribution grid. The application of the FDS in the electric power distribution systems is mainly aimed at precise detecting and locating of the deteriorated components, thereby enhancing the quality and reliability of the electric power delivered to the customers. The impacts of two types of the FDS on distribution system reliability are compared and presented in this paper. The first type is a representative of the FDS which diagnoses the deteriorated components after their failing. However, the second type is a representative of... 

ECG denoising has always been an important issue in medical engineering. The purposes of denoising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal. This paper proposes a method for removing white Gaussian noise from ECG signals. The concepts of singularity and local maxima of the wavelet transform modulus were used for analyzing singularity and reconstructing the ECG signal. Adaptive thresholding was used to remove white Gaussian noise modulus maximum of wavelet transform and then reconstruct the signal  

Melanoma is the deadliest form of skin cancer if not treated early. The best way to cure melanoma is to treat it in its earliest stage of development. Since melanoma is similar to benign moles in its shape and appearance, it is often mistaken for moles and left untreated. Automatic melanoma detection is an essential way to increase the survival rate of patients by detecting melanoma in its early stages. In this paper, a new method for automatic diagnosis of melanoma using segmented dermatoscopic images is provided. Almost all related methods follow similar approaches but using different features. We have introduced several new features which could improve the accuracy of diagnosing melanoma.... 

Electromyogram signal is a biopotential signal that may be measured on the surface of contracting muscles representing neuromuscular activities. This signal may be utilized in various applications such as clinical diagnosis of diseased neuromuscular systems and as a measurement tool for evaluation of rehabilitation activities. Another recent application is the usage of EMG signal in design and implementation of neural controlled prosthesis hands. For this purpose appropriate features of EMG signal are required such that intended hand movements may be recognized correctly. In this work we considered a new method based on quadratic time-frequency representation namely Wigner-Ville distribution... 

Precipitation and scaling of calcium sulfate have been known as major problems facing process industries and oilfield operations. Most scale prediction models are based on aqueous thermodynamics and solubility behavior of salts in aqueous electrolyte solutions. There is yet a huge interest in developing reliable, simple, and accurate solubility prediction models. In this study, a comprehensive model based on least-squares support vector machine (LS-SVM) is presented, which is mainly devoted to calcium sulfate dihydrate (or gypsum) solubility in aqueous solutions of mixed electrolytes covering wide temperature ranges. In this respect, an aggregate of 880 experimental data were gathered from... 

A hybrid algorithm of a particle swarm optimization and a fuzzy rule-based classification system is proposed in this study to diagnose breast cancer. Two orthogonal and triangular types of fuzzy sets are applied to represent the input variables. In additional, different input membership functions are considered to increase the classification accuracy. The performance of the proposed hybrid algorithm is studied using a classification accuracy measure on the Wisconsin breast cancer dataset. The results of the comparison using different training data sets show the higher performance of the proposed methodology  

The most common heart disease is Coronary artery disease (CAD). CAD is one of the main causes of heart attacks and deaths across the globe. Early diagnosis of this disease is therefore, of great importance. A large number of methods have thus far been devised for diagnosing CAD. Most of these techniques have been conducted on the basis of the Irvine dataset (University of California), which not only has a limited number of features but is also full of missing values and thus lacks reliability. The present study was designed to seek a new set, free from missing values, comprising features such as the functional class, dyspnea, Q wave, ST elevation, ST depression, and T inversion. Information...