Fault diagnosis

دسترسی آزاد مقاله

1 - تشخیص عیوب ماشینهای دوار با آنالیز ارتعاشات و استفاده از شبکه ‏عصبی
سید مجید عطایی اردستانی

مبنای تشخیص معایب احتمالی یک ماشین، مقایسه طیف‌های فرکانسی ارتعاشات نقاط مختلف آن با طیف‌های مرجع موجود است. استفاده از این روش عیب‌یابی مقرون به صرفه است چرا که بدون نیاز به توقف ماشین، می‌توان وضعیت نقاط مختلف آن را تحت بررسی قرار داد و همچنین فقط در مواقع لازم و چکیده کامل

مبنای تشخیص معایب احتمالی یک ماشین، مقایسه طیف‌های فرکانسی ارتعاشات نقاط مختلف آن با طیف‌های مرجع موجود است. استفاده از این روش عیب‌یابی مقرون به صرفه است چرا که بدون نیاز به توقف ماشین، می‌توان وضعیت نقاط مختلف آن را تحت بررسی قرار داد و همچنین فقط در مواقع لازم و با توجه به میزان پیشرفت عیوب احتمالی، می‌توان اقدام به تعمیر آن نمود. در این تحقیق، از شبکه‌ی عصبی پرسپترون چند لایه ( MLP ) و شبکه عصبی پیشخور ( FNN ) استفاده شده است. همچنین عیوب متداول در ماشین‌آلات دوار بطور جداگانه ایجاد شد و فرکانس ارتعاشی تولیدی توسط دستگاه آنالیزور ADASH 4400&lrm; اندازه‌گیری گردید. با معرفی چهار ویژگی ارتعاشی شامل ناهمراستایی زاویه‌ای، لقی، خرابی و نابالانسی بیرینگ بعنوان داده‌های ورودی به شبکه‌های عصبی مصنوعی، نتایج با سیگنالهای فرکانسی مرجع مقایسه گردید. &lrm; &lrm;نتایج نشان می‌دهد که شبکه‌های عصبی MLP و FNN به ترتیب تا 73% و 78% توانایی تشخیص عیوب را دارند. بنابراین روش FNN برای پیش‌بینی و شناسایی عمر مفید قطعات دوار پیشنهاد می‌گردد. پرونده مقاله

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2 - An LPV Approach to Sensor Fault Diagnosis of Robotic Arm
Amir Hossein Sabbaghan Amir Hossein Hassanabadi

One of the major challenges in robotic arms is to diagnosis sensor fault. To address this challenge, this paper presents an LPV approach. Initially, the dynamics of a two-link manipulator is modelled with a polytopic linear parameter varying structure and then by using چکیده کامل

One of the major challenges in robotic arms is to diagnosis sensor fault. To address this challenge, this paper presents an LPV approach. Initially, the dynamics of a two-link manipulator is modelled with a polytopic linear parameter varying structure and then by using a descriptor system approach and a robust design of a suitable unknown input observer by means of pole placement method along with linear matrix inequalities, in addition to providing an estimate of state variables for using in state feedback, the detection, isolation, and identification of sensor faults in the manipulator are addressed. The proposed observer provides a robust estimate of the faults along with attenuating the disturbance effects. Further, the desired angles of the joints are calculated for achieving the desired trajectory of the robot’s end-effector using the inverse kinematics and by designing a suitable state feedback law with integral mode, the reference signals are tracked. The sufficient condition for stability of the closed-loop system is obtained as a set of linear matrix inequalities at the vertices of the system. The efficiency and effectiveness of the control system, along with the designed fault diagnosis unit, are shown using numerical simulations. پرونده مقاله

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3 - Fault Diagnosis Operator in Linear Fractional Order Singular Systems ‎Using Singular Observer and Unknown Input
F. PourDadashi Komachali M. Shafiee

The singular systems appear in many real occasions of system modeling. Fault occurrence is inevitable in real system; thus to avoid their destructive impacts, new design perspective must be taken. Performance and sensitivity of the fault diagnosis model based methods, h چکیده کامل

The singular systems appear in many real occasions of system modeling. Fault occurrence is inevitable in real system; thus to avoid their destructive impacts, new design perspective must be taken. Performance and sensitivity of the fault diagnosis model based methods, however, significantly dependent on the accuracy of the model. In the one hand, it has been shown that many systems naturally follows the fractional order behavior, while on the other, in some scenarios, fractional modeling has improved the accuracy of the model. In this paper, we pay attention to the fault diagnosis in the fractional order singular systems. To this end, a singular observer with an unknown input has been used for diagnosis of the fault in the fractional order singular system, and the proposed observer convergence will be derived in the form of a linear matrix inequality. An advantage of the proposed method is separation of noise from the desired signal, both in inputs and outputs, using only the inputs and outputs signals. پرونده مقاله

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4 - مروری بر روش‌های تشخیص خطا و تعیین سرمنشأ آن در شبکه‌های توزیع برق
میلاد صمدی شادلو

به مجموعه روش‌هایی که بعد از بروز خطا، موقعیت خطا را با توجه به شرایط قبل و بعد از خطا تشخیص می‌دهند، الگوریتم‌های مکان‌یابی خطا گفته می‌شود و بخشی که این کار را انجام می‌دهد، مکان‌یاب خطا نامیده می‌شود. در همین رابطه مفاهیمی چون تشخیص خطا و جداسازی آن و تشخیص خطا و سرم چکیده کامل

به مجموعه روش‌هایی که بعد از بروز خطا، موقعیت خطا را با توجه به شرایط قبل و بعد از خطا تشخیص می‌دهند، الگوریتم‌های مکان‌یابی خطا گفته می‌شود و بخشی که این کار را انجام می‌دهد، مکان‌یاب خطا نامیده می‌شود. در همین رابطه مفاهیمی چون تشخیص خطا و جداسازی آن و تشخیص خطا و سرمنشأ آن نیز در سیستم قدرت مطرح شده است. تا کنون روش‌های مختلفی به منظور تشخیص خطا و سرمنشأ آن در بخش‌های مختلف سیستم قدرت و نیز تجهیزات آن نظیر ترانسفورماتورها، مبدل‌ها، خطوط هوایی، کابل‌های زمینی، فیدرها، مدارشکن‌ها، رله‌های حفاظتی، ژنراتورها، توربین‌ها و غیره معرفی شده است که هر کدام در تکمیل کارهای گذشته، روشی جدید و کارآمدتر را پیشنهاد داده‌اند. در این مقاله، مطالعه جامعی روی روش‌های تشخص خطا و تعیین سرمنشأ آن در سیستم‌های توزیع برق ارائه شده است. همچنین دسته‌بندی و روش‌شناسی تحقیقات صورت گرفته پیرامون این موضوع در مقالات مختلف، بیان شده است. الگوریتم‌های تشخیص خطا و تعیین مکان خطا از دو دیدگاه کلی تقسیم‌بندی شده و ویژگی‌های هر دسته به صورت کامل تشریح شده است. پرونده مقاله

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5 - Fault diagnosis in a distillation column using a support vector machine based classifier
ebrahim mirakhorli

Fault diagnosis has always been an essential aspect of control system design. This is necessary due to the growing demand for increased performance and safety of industrial systems is discussed. Support vector machine classifier is a new technique based on statistical l چکیده کامل

Fault diagnosis has always been an essential aspect of control system design. This is necessary due to the growing demand for increased performance and safety of industrial systems is discussed. Support vector machine classifier is a new technique based on statistical learning theory and is designed to reduce structural bias. Support vector machine classification in many applications in various fields of machine learning has been successful and appears to be effective for fault diagnosis in industrial systems. This project is to design a support vector machine fault diagnosis system for a distillation tower as a key component of the process. The study included 41 stage distillation condenser and boiler theory is that a combination of two partial products of 99% purity breaks Based on the calculations, modeling and simulation is a tray to tray. Considering the variety of different origins faults in the system under study, a multi-class classification problem can be achieved two techniques commonly used to solve multi-class classification for support vector machine as "one to one" and "one against all" is used. The classifier models designed to detect faults in the systems studied were evaluated as successful results were obtained for all types of faults. The model was designed based on the speed in detecting various faults were compared on the basis of support vector machine model based on a technique called "One on One" have delivered a better performance. پرونده مقاله

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6 - AN INTELLIGENT FAULT DIAGNOSIS APPROACH FOR GEARS AND BEARINGS BASED ON WAVELET TRANSFORM AS A PREPROCESSOR AND ARTIFICIAL NEURAL NETWORKS
Mahmuod Akbari Hadi Homaei Mohammad Heidari

In this paper, a fault diagnosis system based on discrete wavelet transform (DWT) and artificial neural networks (ANNs) is designed to diagnose different types of fault in gears and bearings. DWT is an advanced signal-processing technique for fault detection and identif چکیده کامل

In this paper, a fault diagnosis system based on discrete wavelet transform (DWT) and artificial neural networks (ANNs) is designed to diagnose different types of fault in gears and bearings. DWT is an advanced signal-processing technique for fault detection and identification. Five features of wavelet transform RMS, crest factor, kurtosis, standard deviation and skewness of discrete wavelet coefficients of normalized vibration signals has been selected. These features are considered as the feature vector for training purpose of the ANN. A wavelet selection criteria, Maximum Energy to Shannon Entropy ratio, is used to select an appropriate mother wavelet and discrete level, for feature extraction. To ameliorate the algorithm, various ANNs were exploited to optimize the algorithm so as to determine the best values for ‘‘number of neurons in hidden layer” resulted in a high-speed, meticulous three-layer ANN with a small-sized structure. The diagnosis success rate of this ANN was 100% for experimental data set. Some experimental set of data has been used to verify the effectiveness and accuracy of the proposed method. To develop this method in general fault diagnosis application, three different examples were investigated in cement industry. In first example a MLP network with well-formed and optimized structure (20:15:7) and remarkable accuracy was presented providing the capability to identify different faults of gears and bearings. In second example a neural network with optimized structure (20:15:4) was presented to identify different faults of bearings and in third example an optimized network (20:15:3) was presented to diagnose different faults of gears. The performance of the neural networks in learning, classifying and general fault diagnosis were found encouraging and can be concluded that neural networks have high potential in condition monitoring of the gears and bearings with various faults. پرونده مقاله

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