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diagnosis
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Structural Health Monitoring of Buried Pipelines Using Piezoelectric Sensor
, M.Sc. Thesis Sharif University of Technology ; Zabihollah, Abolghasem (Supervisor)
Abstract
Large-size buried pipelines are an efficient way of transporting of water, waste water, sewage, gas, and oil resources in across the world. Since the buried pipe lines are exposed to many unexpected conditions, such as landslides, corrosion, fatigue, unexpected loading, earthquakes, material flaws or even intentional damaging, so the inspection requirements lead to adoption of new method of maintenance, protection and conserving of pipelines. This proposed project aims to improve the trustworthiness, reliable, yet economical technologies for monitoring of behavior of buried pipe lines during operation and assessing the risk of pipe lines failure. Piezoelectric sensor is bonded on the surface...
Applying Infrared Thermography for Monitoring the Condition and Failure Diagnosis of Power Network
, M.Sc. Thesis Sharif University of Technology ; Ghodrati, Behzad (Supervisor)
Abstract
Temperature and the resulting thermal behavior of electric power generation and distribution equipment and industrial electrical systems and processes are the most critical factors in the reliability of any operation or facility. Temperature is by far the most measured quantity in any industrial environment. For these reasons, monitoring the thermal operating condition of electrical and electromechanical equipment is considered to be a key factor to increasing operational reliability. Infrared thermography (IR/T) as a condition monitoring technique is used to remotely gather thermal information for monitoring the condition of virtually all of the electrical components on an entire system and...
Vibration-Based Fault-Diagnosis of Rotating Machinery Relying on Frequency Transforms Time -
, M.Sc. Thesis Sharif University of Technology ; Manzuri, Mohammad Taghi (Supervisor)
Abstract
This thesis presents several methods time-frequency based approach for classifying the vibration signals of rotatery machine. It uses the features extracted from the time-frequency distribution (TFD) of the vibration signal segments. Results of applying the method to a database of real signals reveal that, for the given classification task, the selected features consistently exhibit a high degree of discrimination between the vibration signals collected from healthy and fault machine. A comparison between the performances of the features extracted from several TFDs shows that the STFT slightly outperforms other reduced interference TFDs.
Development of a Fault Detection Algorithm for a PFI Engine Based on ECU Output Signals
, M.Sc. Thesis Sharif University of Technology ; Hosseini, Vahid (Supervisor) ; Saidi, Mohammad Hassan (Supervisor)
Abstract
Electronic engine control unit (ECU) operates the different actuators using information signals received from the different sensors. Also in the case of any fault in the sensors and actuators operation ECU is responsible to detect and alert the accruing faults. Most of the common faults in the engine such as leakage in intake system and ignition system are not detectable by ECU, on the other hand ECU fault codes some times are hard to interpret or misleading. So fault diagnosis is usually based on experience of repair staff, and might be time consuming and inaccurate. In this study, using the knowledge of premixed fuel injection SI engines and emission generation and statistical data,...
Condition Monitoring and Fault Diagnosis of Rolling Element Bearing in Phase Space
, M.Sc. Thesis Sharif University of Technology ; Behzad, Mehdi (Supervisor)
Abstract
The failure in rolling element bearings as an important part of rotary machine can lead to machine breakdown.Consequently, bearings can reduce reliability of these components and therefore a maintenance strategy is essential to keep reliability in a desired level. The first and primitive strategy is called breakdown or unplanned maintenance. The next is preventive or planned maintenance. In this scheme, maintenance is done after a specified time period irrespective of machine health status. Out of place maintenance is the disadvantage of preventive maintenance. The most effective generation of maintenance strategy is condition based maintenance (CBM) which suggests maintenance decision based...
Fuzzy ANN Approach to Support Independent Failures of Rotary Equipment Based on Vibration, Temperature Parameters-Case Study: A Centrifugal Pump in offshore Industry
, M.Sc. Thesis Sharif University of Technology ; Ghaemi Osgouie, Kambiz (Supervisor) ; Ebrahimi Pour, Vahid (Co-Advisor)
Abstract
Pump operating problems may be either hydraulic or mechanical and there is interdependence between the failure diagnoses of these two categories. Consequently, a correct diagnosis of a pump failure needs to consider many symptoms including hydraulic or mechanical causes. Nonlinear, time-varying behavior, and imprecise measurement information of the systems makes it difficult to deal with pump failures with precise mathematical equations, while human operators with the aid of their practical experience can handle these complex situations, with only a set of imprecise linguistic if-then rules and imprecise system states, but this procedure is time consuming and needs the knowledge of human...
Developing an Artificial Intelligence Algorithm for Diagnosis and Prognosis of Failures
, M.Sc. Thesis Sharif University of Technology ; Houshmand, Mahmood (Supervisor) ; Fattahi, Omid (Co-Advisor)
Abstract
Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of health of a battery. However, the capacity of a lithium-ion battery onboard is difficult to monitor. This paper presents a data-driven approach for capacity estimation. First, new features are extracted from cyclic charge/discharge cycles and used as health indicators. Three algorithms are used to characterize the relationship between extracted features and battery capacity. Decision tree, random forest and boosting algorithms are trained using a...
Study, Research and Diagnosing of Oil and Gas Production Supply by Application of a System Dynamic Approach with Concentration on Supply Chain Collaboration
, M.Sc. Thesis Sharif University of Technology ; Fatahi, Omid (Supervisor) ; Khorami, Hamed ($item.subfieldsMap.e)
Abstract
In today’s competitive market, organizations need to modify their local perspective on enterprise, to a wider scope including supply chain. Focusing on supply chain, helps a more effective and efficient management for organizations. Today’s supply chain management is an inevitable part of enterprise management for achievement of integration between a supply chain’s partners. Oil and gas supply chain, as a complex supply chain, needs an effective and efficient management which uses from different tools and techniques, diagnoses the related errors and drawbacks among upstream partners. A management with such characteristics is scarce, and in this thesis it is tried to present an effective and...
Comparison of the Performance of Accelerometer and Non-Contact Displacement Sensor in Fault Detection of Ball Bearing
, M.Sc. Thesis Sharif University of Technology ; Behzad, Mahdi (Supervisor)
Abstract
The use of multiple sensors that produce different physical parameters of the measured system for its health monitoring raises the reliability of the diagnosis. At the moment, the fault detection capacities of ball bearings by means of proximity probe are detected by exploiting the advantages and reducing its defects by appropriate signal processing of the raw data in the time domain. Also, the application of numerical derivation and integration to achieve to the desired frequency spectrum in defect detection is investigated. A set of experiments with different sizes of internal ring, outer ring and ball defects, four levels of speed and two load levels have been performed. Data acquisition...
Fault Diagnosis of Rotating Machinery using Multi-Sensor Data
, M.Sc. Thesis Sharif University of Technology ; 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...