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    Fault Diagnosis of Rotating Machinery using Multi-Sensor Data

    , M.Sc. Thesis Sharif University of Technology Hekmat Golmakani, Saeed (Author) ; Arghand, Hesamoddin (Supervisor) ; Behzad, Mehdi (Supervisor)
    Abstract
    Vibration signals have been used for fault diagnosis frequently. These signals are captured using sensors, which are mounted on different locations of the equipment. Using data captured from different locations of the equipment, will result in roubustness of the fault diagnosis model. But sometimes the predictions of sensors mounted on different locations, happen to conflict. These conflicts among sources of information, make it hard to decide about the overal health state of the equipment. In fact, these conflicts causes a kind of uncertainty called epistemic uncertainty. A powerful mathematical framework for managing epistemic uncertainties, is Dempster-Shafer theory of evidence. An... 

    The Study of Quark-Gluon Plasma by Flow Harmonics in Heavy Ion Collisions

    , Ph.D. Dissertation Sharif University of Technology Mehrabpour, Hadi (Author) ; Rouhani, Shahin (Supervisor) ; Arfaei, Hesamoddin (Supervisor)
    Abstract
    A new phase of matter which is known as quark-gluon plasma is created after ultrarelativistic nucleus-nucleus collisions. Experimental data imply that hydrodynamic equations can explain this new phase of matter. On the other hand, observations show that the geometry of initial energy distribution fluctuates event-by-event, even in a given centrality class. Also, the geometry of initial state distribution reflects itself as different samples of final particle spectrum results by the hydrodynamic evolution. The particle momentum distribution can be expanded by Fourier series which its coefficients called harmonic flows. We can only study the effects of hydrodynamics on initial fireball by flow... 

    An application of soft computing in oil condition monitoring

    , Article Industrial and Applied Mathematics ; Volume Part F2111 , 2023 , Pages 117-129 ; 23646837 (ISSN) Afsharnia, F ; Behzad, M ; Arghand, H. A ; Sharif University of Technology
    Springer  2023
    Abstract
    Preventive maintenance strategy can reduce the exorbitant costs of purchasing spare parts, repairs, and consequently downtime, as well as increase efficiency and income by reducing downtime. Oil monitoring is one of the most important policies for preventive maintenance of equipment. This chapter aimed to develop a fuzzy program based on engine oil analysis to investigate the erosive behavior of the engine as well as identify the engine condition. Once 1500 engine oil samples were analyzed, the wear debris was measured in oil including iron, copper, aluminum, lead, tin, silicon, PQ, water content, viscosity, and alkalinity of oil, and the suitable information for analysis was obtained. The... 

    Estimation of remaining useful life of rolling element bearings using wavelet packet decomposition and artificial neural network

    , Article Iranian Journal of Science and Technology - Transactions of Electrical Engineering ; Volume 43 , 2019 , Pages 233-245 ; 22286179 (ISSN) Rohani Bastami, A ; Aasi, A ; Arghand, H. A ; Sharif University of Technology
    Springer International Publishing  2019
    Abstract
    Rolling element bearings (REBs) are usually considered among the most critical elements of rotating machines. Therefore, accurate prediction of remaining useful life (RUL) of REBs is a fundamental challenge to improve reliability of the machines. Vibration condition monitoring is the most popular method used for diagnosis of REBs and this is a motivating fact to use recorded vibration data in RUL prediction too. However, it is necessary to extract appropriate features from vibration signal that represent actual damage progress in the REB. In this paper, wavelet packet transform is used to extract signal features and artificial neural network is applied to estimate RUL of the REB. To obtain... 

    Inaccessible rolling bearing diagnosis using a novel criterion for Morlet wavelet optimization

    , Article JVC/Journal of Vibration and Control ; 2021 ; 10775463 (ISSN) Behzad, M ; Kiakojouri, A ; Addin Arghand, H ; Davoodabadi, A ; Sharif University of Technology
    SAGE Publications Inc  2021
    Abstract
    The objective of this research is to diagnose an inaccessible rolling bearing by indirect vibration measurement. In this study, a shaft supported with several bearings is considered. It is assumed that the vibration for at least one bearing is not recordable. The purpose is to diagnose inaccessible bearing by the recorded data from the sensors located on the other bearings. To achieve this goal, the continuous wavelet transform is used to detect weak signatures in the available vibration signals. A new criterion for adjusting the scale parameter of continuous wavelet transform is proposed based on the amplitude of the bearing characteristic frequencies. In this criterion, the optimal scale... 

    Inaccessible rolling bearing diagnosis using a novel criterion for Morlet wavelet optimization

    , Article JVC/Journal of Vibration and Control ; Volume 28, Issue 11-12 , 2022 , Pages 1239-1250 ; 10775463 (ISSN) Behzad, M ; Kiakojouri, A ; Arghand, H. A ; Davoodabadi, A ; Sharif University of Technology
    SAGE Publications Inc  2022
    Abstract
    The objective of this research is to diagnose an inaccessible rolling bearing by indirect vibration measurement. In this study, a shaft supported with several bearings is considered. It is assumed that the vibration for at least one bearing is not recordable. The purpose is to diagnose inaccessible bearing by the recorded data from the sensors located on the other bearings. To achieve this goal, the continuous wavelet transform is used to detect weak signatures in the available vibration signals. A new criterion for adjusting the scale parameter of continuous wavelet transform is proposed based on the amplitude of the bearing characteristic frequencies. In this criterion, the optimal scale... 

    Speed Estimation for Fault Diagnosis in Machinery Using Vibration Signal

    , M.Sc. Thesis Sharif University of Technology Izanlo, Hassan (Author) ; Behzad, Mehdi (Supervisor) ; Arghand, Hesamaddin (Co-Supervisor)
    Abstract
    Nowadays, condition monitoring and fault diagnosis are considered as one of the most important maintenance and diagnosis strategies in state-of-the-art industries. Serious damage such as machine and coupled equipment failure, machine failure in a neighborhood in case of severe damage, and production loss will be resulted in late fault detection. One of the critical information in condition monitoring is the machine's rotating speed. In many cases of condition monitoring, the rotating speed, and the machine's technical information (such as bearing characteristic frequency )is not available. In this study, a ruled-base model and an intelligent model are developed for machine running speed... 

    Failure threshold determination of rolling element bearings using vibration fluctuation and failure modes

    , Article Applied Sciences (Switzerland) ; Volume 11, Issue 1 , 2021 , Pages 1-18 ; 20763417 (ISSN) Behzad, M ; Feizhoseini, S ; Addin Arghand, H ; Davoodabadi, A ; Mba, D ; Sharif University of Technology
    MDPI AG  2021
    Abstract
    One of the challenges in predicting the remaining useful life (RUL) of rolling element bearings (REBs) is determining a proper failure threshold (FT). In the literature, the FT is usually assumed to be a constant value of an extracted feature from the vibration signals. In this study, a degradation indicator was extracted to describe damage to REBs by applying principal component analysis (PCA) to their run-to-failure data. The relationship between this degradation indicator and the vibration peak was represented through a joint probability distribution using statistical copula models. The FT was proposed as a probability distribution based on the fluctuation increase in the vibration trend.... 

    Prediction of Rolling Element Bearings Degradation Trend Using Limited Data

    , M.Sc. Thesis Sharif University of Technology Tajdini, Jalal (Author) ; Behzad, Mehdi (Supervisor) ; Arghand, Hesam Al-din (Co-Supervisor)
    Abstract
    Condition monitoring of machinery is of significant economic importance to mitigate production losses resulting from downtimes. Unforeseen failure of roller element bearings is the most common issue observed in industrial units. However, detecting and tracking the progression of these failures through machine vibration monitoring and predicting the deterioration of these rotating components are viable solutions. Numerous studies have focused on using laboratory accelerated life test data for fault detection and remaining useful life prediction of these components. While online monitoring of all equipment in the industry may not be feasible, and conditions in the field differ from laboratory... 

    Intelligent Fault Diagnosis using Multiple Sensor Data Fusion for Detecting Misalignment and Unbalance

    , M.Sc. Thesis Sharif University of Technology Yadegari, Mohammad Erfan (Author) ; Behzad, Mehdi (Supervisor) ; Arghand, Hesam Al-Din (Co-Supervisor)
    Abstract
    Intelligent predictive maintenance is recognized as a cornerstone of Industry 4.0, where intelligent software is employed for the early detection of faults and the prevention of unexpected failures. Recent research indicates that the integration of multi-sensor data for fault diagnosis of gearboxes and bearings, using artificial intelligence models, has been successful. However, conventional methods face several challenges. These include an over-reliance on the signal characteristics of a single sensor and the impracticality of applying intelligent learning methods, particularly deep learning, despite their high potential, due to the unavailability of sufficiently large and diverse... 

    Bearing housing looseness effect on rotating machinery vibration

    , Article Proceedings of the International Congress on Sound and Vibration ; 2023 ; 23293675 (ISSN); 978-801103423-8 (ISBN) Behzad, M ; Izanlo, H ; Arghand, H. A ; Davoodabadi, A ; Saleh, A ; Carletti E ; Sharif University of Technology
    Society of Acoustics  2023
    Abstract
    Mechanical looseness is one of the common faults in rotating machinery. Wearing in the inner surface of the housing and outer race of the rolling element bearing (REB) increases the clearance and mechanical looseness that may lead to abnormal vibration in the machine. Mechanical looseness reduces the equivalent stiffness of the bearings leading to a reduction in the system's natural frequencies. In this study, finite element analysis (FEA) is used for numerical modeling of the mechanical looseness effect on the vibration characteristics of an industrial electrofan. To this aim, the variation of the shaft center displacement resulting from a specific load is investigated in terms of the... 

    Bearing Prognostics under the Load-Varying Conditions, Using Combination of Data-Drive and Physic-based Methods

    , Ph.D. Dissertation Sharif University of Technology Arghand, Hesamoddin (Author) ; Behzad, Mehdi (Supervisor) ; Rohani, Abbas (Co-Supervisor) ; Ming J. Zuo (Co-Supervisor)
    Abstract
    In this research, an artificial intelligence (AI) based model and a physics-based model have individually employed for prognostics of rolling element bearing (REB) under constant operating condition. Then a combination of physics-based models and data-driven model (Hybrid model) has been developed for remaining useful life (RUL) prediction of rolling element bearing (REB) under the load de-rating condition. The well-known crack propagation model Paris’ law has been employed to develop a physics-based model which can describe the degradation rate of the REB when the applying load is changed. To this aim, the elastic solution of the contact mechanics and Hertz stress between curved surfaces is...