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Remmerbach, Torsten W., Falk Wottawah, Julia Dietrich, Bryan Lincoln, Christian Wittekind, and Jochen Guck. "Oral Cancer Diagnosis by Mechanical Phenotyping." Cancer Research 69, no. 5 (February 17, 2009): 1728–32. http://dx.doi.org/10.1158/0008-5472.can-08-4073.
Donelson, Ronald. "Mechanical Diagnosis and Therapy for Radiculopathy." Physical Medicine and Rehabilitation Clinics of North America 22, no. 1 (February 2011): 75–89. http://dx.doi.org/10.1016/j.pmr.2010.11.001.
With the development of science and technology, the theoretical content of mechanical fault diagnosis technology has been initially improved and established a scientific research system. Combining the mechanical diagnostic techniques with the current advanced science and technology, a variety of mechanical fault diagnosis methods have been researched and developed. Mechanical fault diagnosis evolved from empirical diagnosis to mechanical diagnosis and then to the current intelligent learning diagnosis. Now mechanical fault diagnosis collects mechanical failure data precisely mainly by a variety of sensors, uses a variety of fault diagnosis model to conduct diversified and intelligent diagnosis.
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Jing, Hong Lei, Jing Nie, and Nian Zhang. "Augmented-Reality-Based Mechanical Fault Diagnosis Method." Advanced Materials Research 1044-1045 (October 2014): 720–22. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.720.
With the rapid development of modern society, the industrial mechanized production reached unprecedented climax in this era. Science and technology advance increasingly, modern equipment from structure to function tends to be complex and improved, and gradually achieve a high degree of automation. However, due to the inevitable factors such as wear and tear, abrasion and chemicals infection, machinery equipment will inevitably appear unforeseen fault, causing the machine to detract from the performance, or even causing serious economic losses. Therefore, mechanical fault diagnosis can reduce equipment accident rate and ensure the long-term stable operation of the device. And applying the augmented reality to machinery fault diagnosis method research can maximize the efficiency of mechanical fault diagnosis and equipment efficiency. This article explores the prospects for the development of mechanical fault diagnosis methods based on the theoretical basis and application value of augmented reality.
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Yu, Guangwei, Xianzhi Wang, and Chunlin Da. "Compound mechanical fault diagnosis based on CMDE." Advances in Mechanical Engineering 14, no. 2 (February 2022): 168781322210805. http://dx.doi.org/10.1177/16878132221080560.
The fault diagnosis technique is of important for the safety operation of the rotating machinery. In the fault diagnosis framework, the entropy-based method is a promising tool for the feature extraction and signal processing. Among the entropy-based methods, the diversity entropy has arisen increasing attention due to its merits of high consistency, strong robustness, and high calculation efficiency. However, it suffers the defect that the multiscale procedure leads to unstable complexity estimation at higher scales. This induces a poor cluster performance in analyzing the compound mechanical fault signals. To address this issue, this paper presents a novel feature extraction method called composite multiscale diversity entropy (CMDE). The proposed CMDE utilizes the mean complexity value of multiple sliding windows for each scale to enhance the stability, which enables the diversity entropy could dig richer fault information from deeper scales for the compound fault diagnosis of rotating machinery. Then, the stability of CMDE has been evaluated using synthetic gear signals. At last, the proposed CMDE has been applied in the compound mechanical fault diagnosis. The experimental results show that the CMDE achieves the highest diagnosis accuracy compared to the existing entropy-based feature extraction methods.
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Xu, Gang. "MECHANICAL FAILURE DIAGNOSIS IN UNSTEADY OPERATING CONDITIONS." Chinese Journal of Mechanical Engineering 37, no. 12 (2001): 104. http://dx.doi.org/10.3901/jme.2001.12.104.
Wu, Wen Bing, Shu Qun Yang, and Yi Jian Huang. "Application of Bipectrum in Mechanical Fault Diagnosis." Advanced Materials Research 538-541 (June 2012): 2645–48. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.2645.
Bispectrum produced by third order cumulant contains the asymmetric and nonlinear information of signal, which can be used to describe the nonlinear phase coupling, especially the quadratic phase coupling, has been widely applied in fault diagnosis. The features of fault signals and normal signals are fetched by 2-D wavelet in different directions, then these features are used to diagnose fault. The experiment shows that the method can achieve satisfactory result.
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CHEN, Yushu. "Nonlinear dynamical principle of mechanical fault diagnosis." Chinese Journal of Mechanical Engineering 43, no. 01 (2007): 25. http://dx.doi.org/10.3901/jme.2007.01.025.
Gelman, L., S. Gorpinich, and C. Thompson. "Adaptive diagnosis of the bilinear mechanical systems." Mechanical Systems and Signal Processing 23, no. 5 (July 2009): 1548–53. http://dx.doi.org/10.1016/j.ymssp.2009.01.007.
Remmerbach, T. W., J. Guck, and J. Käs. "O33. Oral cancer diagnosis by mechanical phenotyping." Oral Oncology 47 (July 2011): S39. http://dx.doi.org/10.1016/j.oraloncology.2011.06.144.
May, Stephen J. "Development of aspects of mechanical diagnosis and therapy." Thesis, Sheffield Hallam University, 2009. http://shura.shu.ac.uk/20757/.
Mechanical Diagnosis and Therapy is a system of classification, assessment and management applied to all musculoskeletal problems that is used by clinicians worldwide. The first section concerns the up-dating and contextualising of Mechanical Diagnosis and Therapy (MDT). The books, co-authored with the founder of MDT, Robin McKenzie, applied the principles to extremity musculoskeletal problems, and then set MDT in the contemporary evidence-based background for lumbar, cervical and thoracic problems. This involved an up-dating of the classification system, as well as synthesis and analysis of aspects of musculoskeletal medicine. The second section presents a patient perspective on musculoskeletal problems. This involves an exploration of patient opinions about back pain and its management, and an audit of outcomes in a clinical setting in which an active exercise-based treatment approach was applied. The third section relates to a number of publications that sought to validate aspects of MDT. Centralisation is a key finding during the assessment of spinal patients and work on this included a systematic review of the relevant literature and an analysis of centralisation in patients with sciatica. In another study we conducted a secondary analysis of a published trial to see what happened if patients were crossed-over from exercises that were unmatched with directional preference to matched exercises. We published a case-control study that validates the postural syndrome and measured the prevalence rates of MDT classifications in the patient population. Reliability is a keycomponent of any musculoskeletal assessment system in which clinicians are making management decisions based on physical examination procedures - a systematic review that detailed the reliability of MDT compared to other commonly used examination procedures was an important contribution to the literature. Overall these works have led to a significant independent and original contribution to knowledge and understanding of MDT.
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Calis, Hakan. "Current based detection of mechanical faults in induction motors." Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300597.
This thesis develops the concept of qualitative model-based fault diagnosis technique and applies it to extrusion blow molding. The extrusion blow molding process is reviewed along with theories of qualitative reasoning, from which process-based qualitative models are developed. Based on the models, a fault diagnosis system is developed consisting of two parts: a fault reasoning system based on a simulation of the EBM process and a process monitor to measure process variables and to adapt the diagnosis to changing environments.
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Wortman, Tyler David. "LesionAir : a low-cost tool for automated skin cancer diagnosis and mapping." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104499.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016. Cataloged from PDF version of thesis. Includes bibliographical references (pages 235-252). Skin cancer is the most common form of cancer in the United States; one out of every five Americans develops skin cancer at some point in their lifetime. Diagnosing cancerous lesions early is critical as it significantly increases the chance of survival. However, current techniques for diagnosing skin cancer lack specificity and sensitivity, resulting in many unnecessary biopsies and missed diagnoses. Although some researchers have increased diagnostic efficacy by quantitatively diagnosing skin cancer in an automated fashion, these methods require extremely bulky, expensive, and complicated equipment. This thesis presents the design and testing of LesionAir, a small, low-cost skin cancer diagnostic tool that measures the full-field compliance of the skin - which is well known to correlate strongly with skin cancer - by applying a vacuum force to the tissue and measuring precise deflection using structured light 3D reconstruction. Image processing algorithms determine additional morphological information about the potentially cancerous lesion. A pilot study of ten patients with suspect lesions validated LesionAir's effectiveness. After biopsy and analysis, a dermatopathologist confirmed the diagnosis of skin cancer in tissue that LesionAir identified as noticeably stiffer, and the regions of this stiffened tissue aligned nearly perfectly with the bounds established by the histological tests, which showed the method determines the precise coordinates that must be excised to safely remove all cancerous tissue. This technology can thus enable patients, primary care physicians, and dermatologists to rapidly identify and diagnose skin cancer with diagnostic quality not seen before from any equipment in this class. by Tyler David Wortman. Ph. D.
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Balden, Victor. "The identification of structural modal parameters, as an alternative in-vivo diagnosis for osteoporosis." Master's thesis, University of Cape Town, 1997. http://hdl.handle.net/11427/9503.
Includes bibliographies. An alternative non-invasive diagnostic technique was sought for the diagnosis of osteoporosis in human subjects. The tibia vibration technique was proposed after reviewing the literature on detection techniques for osteoporosis. The basis of diagnosis of the tibia vibration technique is the measured resonant frequency of the patient's tibia. The patient's tibia is excited, generally by means of an impact hammer, while the response is captured and resonant frequencies extracted. This dissertation does not attempt to measure the resonant frequencies of a human tibia, but rather develop and validate the required experimental protocol and system identification procedures, on a simple test structure. A theoretical finite element model of the test structure was developed to ensure that both the experimental protocol and system identification procedures provided accurate results. The impulse response technique was adopted to excite the test structure.
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Zhongyi, Cai. "Vibration diagnosis of elastic shafts with a transverse crack." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2011. https://ro.ecu.edu.au/theses/405.
Detection of the shaft crack in a rotating machine is one of the most challenging problems in equipment predictive maintenance. In the available literature, various crack detection methods have been applied to study the dynamic behaviour of a cracked shaft. This study sought to attempt a vibration-based method. Elastic shafts with three different types of transverse cracks, including experimentally-induced fatigue crack, welded shaft crack, and wire-cut crack, were fabricated, and used to analyse the bending stiffness and frequency response in the vertical direction. The results from the cracked shafts were compared with that of an intact shaft. Bending stiffness of different shafts was measured as a function of rotation angle of the shafts. Among the three different crack types, the bending stiffness of the fatigue crack shaft showed a typical breathing behaviour, which was consistent with the previous theoretical results. The welded shaft crack also demonstrated opening and closing characteristics, but the stiffness was found to be much lower compared with that of a fatigue cracked shaft. As for the wire-cut crack, no breathing mechanism was observed for any rotational angle, due to the big width of the gap. Therefore, it is concluded that the fatigue induced crack is the most accurate method to evaluate the vibration characteristics of cracked shafts. Our results also indicated that existing switching model and harmonic models cannot describe the periodic stiffness of a transverse shaft crack accurately. Modal analysis was carried out on the intact shaft, as well as the three types of cracked shafts. Frequency responses in the X-axis direction were obtained. The correlation between the bending stiffness and the resonant frequency was examined, and it was experimentally proved that the decrease in resonant frequency was almost proportional to the reduction in the stiffness. Also, the amplitude of vibration response was found to be amplified by the crack element. The cause and implications of these results were analysed, and they are expected to deepen our understanding of crack diagnosis using vibration method.
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Dellah, Aaron Scott. "Noninvasive diagnosis of acute compartment syndromes using ultrasound and mechanical vibration, feasibility study." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0014/MQ36021.pdf.
CHAO, YUE. "Fault Detection and Diagnosis of Electro-Mechanical Systems Based on Real-time Data." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1215092937.
Pastras, Christopher John. "Assessment of Utricular Nerve, Hair Cell and Mechanical Function, in vivo." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/20075.
Vestibular research currently relies on single response measures such as ex vivo hair cell and in vivo single unit recordings. Although these methods allow detailed insight into the response properties of individual vestibular hair cells and neurons, they do not provide a holistic understanding of peripheral vestibular functioning and its relationship to vestibular pathology in a living system. For this to take place, in vivo recordings of peripheral vestibular nerve, hair cell and mechanical function are needed. The previous inability to record vestibular hair cell responses stemmed from a difficulty in accessing the vestibular end-organs and stimulating them in isolation of the cochlea. To circumvent this, we developed a ventral surgical approach, removing the cochlea, to provide full access to the basal surface of the utricular macula. This allowed functional and mechanical utricular hair cell recordings, alongside gross utricular nerve responses. Recordings were performed in anaesthetized guinea pigs using Bone Conducted Vibration (BCV) and Air Conducted Sound (ACS) stimuli, providing a clinical link to vestibular reflex testing. We have thus far performed experiments involving: 1) Selective manipulation of vestibular nerve function, using electrical stimulation of the central vestibular system. 2) Glass micropipette recordings from the basal surface of the macular epithelium, which provided a robust and localized measure of extracellular utricular hair cell function. 3) With the macular exposed, we have measured the dynamic motion of the macula using Laser Doppler Vibrometry, which was recorded alongside the hair cell and nerve response recordings. 4) We have used physiological and pharmacological experimental manipulations to selectively modulate utricular nerve, hair cell or mechanical function, demonstrating the ability to differentially diagnose the basis of peripheral vestibular disorders in the mammalian utricle. These tools allow for a more complete understanding of peripheral vestibular function and a first order perspective into clinical disorders effecting the otoliths.
Natke, Hans Günther, and Czeslav Cempel. Model-Aided Diagnosis of Mechanical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60413-3.
McKenzie, Robin. The cervical and thoracic spine: Mechanical diagnosis and therapy vol. 1 and 2. 2nd ed. Raumati Beach, N.Z: Spinal Publications (N.Z.) Ltd., 2006.
Godart, Bruno. Guide to Diagnosis and Appraisal of AAR Damage to Concrete in Structures: Part 1 Diagnosis (AAR 6.1). Dordrecht: Springer Netherlands, 2013.
Feret, M. P., and J. I. Glasgow. "Generic Diagnosis for Mechanical Devices." In Applications of Artificial Intelligence in Engineering VI, 753–67. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3648-8_49.
Kwan, A. K. H., and P. L. Ng. "Building Diagnostic Techniques and Building Diagnosis: The Way Forward." In Lecture Notes in Mechanical Engineering, 849–62. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09507-3_74.
Derbel, Syrine, Nabih Feki, Florentina Nicolau, Jean Pierre Barbot, Mohamed Slim Abbes, and Mohamed Haddar. "Diagnosis Methods for Mechatronic Systems." In Lecture Notes in Mechanical Engineering, 43–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46729-6_5.
Varughese, Cyril, Ajith P. Nair, and Jordan Chaisson. "Diagnosis of Device Thrombosis." In Mechanical Circulatory Support for Advanced Heart Failure, 191–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65364-8_12.
Hariharan, V., G. Rajeshkumar, and K. A. Ibrahim Sheriff. "Fault Diagnosis of Unbalanced Multirotor System." In Lecture Notes in Mechanical Engineering, 387–96. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7557-0_34.
Behzad, Mehdi, and Majid Karimi. "Bearing Fault Diagnosis Using Attractor Comparison." In Lecture Notes in Mechanical Engineering, 93–101. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06966-1_9.
Liu, Zhigang, Zhiqiang Long, and Xiaolong Li. "Maglev Train Control and Diagnosis Networks." In Springer Tracts in Mechanical Engineering, 121–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45673-6_5.
Shen, Tunan. "Diagnosis of Mechanical Faults in Electric Machines." In Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles, 67–102. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-36992-7_5.
Saputra, D., and K. Marhadi. "Automatic Fault Diagnosis in Wind Turbine Applications." In Lecture Notes in Mechanical Engineering, 77–86. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9199-0_8.
Тези доповідей конференцій з теми "Mechanical diagnosis":
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Turner, Cameron J. "Diagnosis via NURBs Metamodel." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-38323.
In vivo tissue condition diagnosis is a challenging engineering problem. The goal is to develop a technology that can eliminate tissue removal and external examination and enable less invasive surgical techniques to be used with a precision provided by a knowledge of the tissue within the body. Particularly challenging is the task of automating the diagnosis of the tissue condition. In this work, a metamodeling technique based on Non-Uniform Rational B-splines is used to analyze and automate the diagnosis of human tissue conditions. The resulting diagnoses are compared to results from medical doctors and the challenges in such data analysis are discussed. The technique has implications for both biomedical and electromechical system fault diagnosis and diagnostics.
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Tayyab, Syed Muhammad, Paolo Pennacchi, Steven Chatterton, and Eram Asghar. "Intelligent Defect Diagnosis of Spiral Bevel Gears Under Different Operating Conditions Using ANN and KNN Classifiers." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-70016.
Abstract Spiral bevel gears are important part of many mechanical transmission systems and are known for their smooth operation and strong load carrying capacity. This type of gear has a high contact ratio, which makes it very difficult to diagnose even serious defects. Therefore, spiral bevel gears have rarely been used as a reference for defect diagnosis techniques. To overcome these challenges, artificial intelligence (AI) techniques are used in this research to diagnose defects in spiral bevel gears. Although Al techniques in the field of fault diagnosis have been very successful, however, these methods largely use the assumption that the training and test data come from the same operating conditions. However, when the operating conditions in which the trained model is deployed for predictions, differ from the operating conditions in which the model was trained, the performance of these approaches might be significantly reduced. Outside the laboratory, in real-world applications, operating conditions significantly vary, and it is difficult to obtain data for all potential operating conditions. To overcome this limitation and to make AI techniques suitable for diagnosing spiral bevel gear faults under different operating conditions, an effort is made to find fault distinguishing features, which are lesser sensitive to operating conditions. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers for fault detection. Performance comparison between both classifiers is made to determine their individual capability and suitability for diagnosing defects of spiral bevel gears under different operating conditions.
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Zhou, Dengji, Tingting Wei, Huisheng Zhang, Meishan Chen, Shixi Ma, and Zhenhua Lu. "A Novel Information Fusion Model Based on D-S Evidence Theory for Equipment Diagnosis." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-65292.
With the wide-scale use of mechanical equipment, more and more faults occur. At the same time, data deluge about the conditions of machines come into being with the development of sensor technology and information technology. It provides opportunities and challenges to solve the fault problems of mechanical equipment. Information fusion seems to be a useful solution, which is the process of integration of multiple data and knowledge representing the same object into a consistent, accurate, and useful representation. A novel information fusion model, with hybrid-type fusion architecture, is built in this paper. This model consists of data layer, feature layer and decision layer, based on a new Dempster/Shafer (D-S) evidence algorithm. After the data preprocessing based on event reasoning in data layer and feature layer, the information will be fused based on the new algorithm in feature layer and decision layer. Application of this information fusion model in fault diagnosis is beneficial in two aspects, diagnostic applicability and diagnostic accuracy. An effect can be caused by different faults. This information fusion model can solve this problem and increase the number of recognizable faults, to expand the range of fault diagnosis. Additionally, this model can overcome the uncertainty of information and equipment to increase diagnostic accuracy. Two case studies are implemented by this information fusion model to evaluate it. In the first case, fault probabilities calculated by different methods are adopted as inputs to diagnose a fault, which is quite different to be detected based on the information from a single system. The second case is about sensor fault diagnosis. Fault signals are planted into the measured parameters for the diagnostic system, to test the ability to consider the uncertainty of measured parameters. The case study result shows that the model can identify the fault more effectively and accurately. Meanwhile, it has good expansibility, which may be used in more fields.
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Ruff, David N., and Robert K. Paasch. "Consideration of Failure Diagnosis in Conceptual Design of Mechanical Systems." In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0019.
Abstract A methodology for improving quality and reducing life cycle costs of mechanical systems is described. The principle concept is that a system can be designed, in the conceptual stages, to be easier to diagnose for failures. To perform this, functional decomposition and form to function mapping are utilized both as a model of design and to demonstrate the relation of diagnosis to design. Design guidelines from other disciplines of engineering and concurrent engineering principles are applied to enhance the concept. The affects of the different elements of concurrent engineering on each other are explored. Of the elements of concurrent engineering, design for manufacturability affects the others the greatest. Design for manufacturability principles impact the size of field replaceable units and can improve the ease of failure diagnosis of a system. For a hypothetical case, form to function mapping that would create a difficult to diagnose system is redesigned to reduce the difficulty of failure diagnosis. Further, three diagnosability metrics are developed and four hypothetical conceptual designs are evaluated for diagnosability and compared The area of design for diagnosis offers promise in improving system quality and reducing life cycle cost; research is continuing to refine and integrate the procedures with other components of the concurrent engineering design process.
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Bidini, Gianni, Francesco Fantozzi, and Linda Barelli. "Thermodynamic Diagnosis of a Small Scale Gas Turbine With Neural Networks." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/aes-23653.
Abstract Real time monitoring and the diagnostics of fault critical power plants, such as small scale gas turbine based ones, is proved to increase their availability and rentability. However, the diagnostic tool requires high speed calculators and fast response models to produce time efficient action, therefore encouraging model free application such as Artificial Neural Networks (ANN) techniques. To this aim, the present paper investigates the performances of an ANN based diagnostic system realized for a small scale commercial gas turbine. After a data harvesting campaign on two existing installations, a diagnostic tool (DT), provided by the gas turbine manufacturer, was operated to produce a sufficient comprehensive diagnostic data base to be used to training and testing the ANN system. The DT system evaluates performance deterioration causes (i.e. compressor fouling) modifying diagnostic parameters, such as efficiencies and combustion parameters, in order to meet data provided by thermodynamic simulation with data gathered on field therefore requiring a relatively high number of input variables (namely 9). The results obtained show good agreement with the ones provided by the DT diagnostic tool with a percentage error not exceeding 4%. Moreover, the ANN model utilized in the diagnosis was implemented considering only 3 input parameters therefore resulting in a less complicated system, not considering the dramatic increase in execution time due to the lack of any iterative calculation. The next step is to extend the monitoring to the overall gas turbine’s components and to integrate the monitoring system with an artificial intelligence based supervising system for diagnostic purposes which derives from ANN outputs damage presence, fault source and evaluation.
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Navarrete, N. A., and J. I. Huertas. "Engine Simulator for ECMs Diagnosis." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-68175.
Currently, the number of companies that provide diagnosis, repair and maintenance services to the electronic control modules — ECMs of the vehicles is very limited. Even though the demand of the service is still unsatisfied, the possibility of expansion of the existing companies is limited by the need of an engine simulator to semi-automate the ECMs diagnosis process. To fulfill this requirement, the present paper describes the design, implementation and testing of an electronic device that simulates the electrical signals generated by the sensors and transducers commonly installed on engines. The device was programmed to simulate different models of commercial engines and to perform automatically the standard procedures followed to identify the most common failures of the ECMs. This device incorporates systems to guarantee the safety of the information gathered during the diagnosis process and the physical integrity of the ECMs being diagnosed.
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Correas, Luis, Ángel Martínez, and Antonio Valero. "Operation Diagnosis of a Combined Cycle Based on the Structural Theory of Thermoeconomics." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0848.
Abstract Diagnosis of the performance of energy was theoretically developed based on the Structural Theory (Valero, Serra and Lozano, 1993), and traditionally Thermoeconomics have usually been applied to the design of power plants and comparison between alternatives. However, the application of thermoeconomic techniques to actual power plants has always to face the generally poor quality of measurement readings from the standard field instrumentation as an unavoidable first step. The proposed methodology focuses on measurement uncertainty estimation and performance calculation by means of data reconciliation techniques, in order to obtain the most confident plant balance upon the available instrumentation. The formulation of the Structural Theory has been applied to a combined cycle, where the Fuel-Product relationships at the component level must be optimally defined for a correct malfunction interpretation. This set of relationships determines the ability to diagnose and the level of the diagnostics obtained. The paper reports the application of the methodology to a 280 MW rated combined cycle, where performance diagnosis is illustrated with results from a collection of actual operation data sets. The results show that data reconciliation yields sufficient accuracy to conduct a thermoeconomic analysis, and how the estimated impact on fuel correlates with physical causes. Hence the feasibility of thermoeconomic analysis of plant operation is demonstrated.
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Howell, Adam S., and J. Karl Hedrick. "Multiple Fault Diagnosis as Applied to Automated Vehicle Control." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0083.
Abstract This paper addresses the problem of detecting multiple faults for the longitudinal control system of an automated vehicle. An existing fault diagnostic system which can isolate all single faults is extended to the diagnosis of multiple faults via improved residual processing in the form of fuzzy logic. The new diagnostic system is shown to correctly detect and isolate all single and multiple faults in a subset of the automated vehicle control system components.
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Morita, Masao. "Vibration Analysis on the Rotating Machinery." In ASME 2005 Power Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/pwr2005-50361.
The paper presents vibration analysis on the boiler feed water pump in thermal power plants. In the Tokyo Electric Power Company (TEPCO), mechanical vibration diagnosis of rotating machinery has been carried out, and got good results. However, in some cases, abnormal condition of machine at an initial stage couldn’t specify, or a machine’s life didn’t predict, because of diagnosing only by tendency of vibration severity. Mechanical vibration diagnoses are typical diagnosing method on the operating condition of the rotating machines, which are well known as one of the Condition Based Maintenance (CBM). It has already been established theoretically, however the actual activities of the vibration diagnosis are required special experiences, especially for the prediction of abnormal condition of rotating machine such as imbalance, and misalignment, and also prediction of bearing failures, etc., These diagnostics have been used for about 30 years in various fields of industries. Therefore, both of the condition survey such as tendency management of vibration severity and condition analysis such as frequency analysis and wave analysis, by using vibrometers, are applied for several power plants of the TEPCO.
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Li, C. James, Chong-suhk Lee, and Sun’an Wang. "Diagnosis and Diagnostic Rule Extraction Using Fuzzy Neural Network." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/dsc-24506.
Abstract The goal of this study is to develop a reasoning device and a diagnostic rule extraction methodology based on fuzzy neural network. This paper describes a method to obtain a fuzzy neural network classifier from labeled training data sets and algorithms to extracted linguistic diagnostic rules from such a trained fuzzy neural network. Benchmark comparisons were performed using three data sets from three different fields of applications. The proposed methodology was shown to outperform all the existing methods that were compared.
Звіти організацій з теми "Mechanical diagnosis":
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Feder, R., E. Mazzucato, T. Munsat, H. Park, D. R. Smith, R. Ellis, G. Labik, and C. Priniski. Mechanical Design of the NSTX High-k Scattering Diagnostic. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/899516.
D.I. Simon, E. Marmar, N.L. Bretz, R. Bravenec, and R.F. Parsells. Optical and Mechanical Design of C-Mod Motional Stark Effect Diagnostic. Office of Scientific and Technical Information (OSTI), November 1999. http://dx.doi.org/10.2172/14501.
Morse, J. D., J. C. Koo, R. T. Graff, A. F. Jankowski, and J. P. Hayes. Field-emission cathode micro-electro-mechanical system technology for sensors, diagnostics, and microelectronics. Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/305303.
Lambert, David, William Cooper, Ibrahim Tansel, and Keith Jamison. Particulate Meso-scale Mechanics Diagnostics: Magnetic Sensors for Dynamic State Orientation. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada605742.
Miles, Richard B. Student Training Program in Advanced Diagnostics for High-speed Fluid Mechanics. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada303540.
Xiao, Xingcheng. In situ Diagnostics of Coupled Electrochemical-Mechanical Properties of Solid Electrolyte Interphases on Lithium Metal Rechargeable Batteries. Office of Scientific and Technical Information (OSTI), August 2020. http://dx.doi.org/10.2172/1653427.
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