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Remote monitoring and fault diagnosis of an industrial machine through sensor fusionLang, Haoxiang 05 1900 (has links)
Fault detection and diagnosis is quite important in engineering systems, and deserves further attention in view of the increasing complexity of modern machinery. Traditional single-sensor methods of fault monitoring and diagnosis may find it difficult to meet modern industrial requirements because there is usually no direct way to measure and accurately correlate a machine fault to a single sensor output. Fusion of information from multiple sensors can overcome this shortcoming. In this thesis, a neural-fuzzy approach of multi-sensor fusion is developed for a network-enabled remote fault diagnosis system. The approach is validated by applying it to an industrial machine called the Iron Butcher, which is a machine used in the fish processing industry for the removal of the head in fish prior to further processing for canning.
An important characteristic of the fault diagnosis approach developed in this thesis is to make an accurate decision of the machine condition by fusing information from different sensors. First, sound, vibration and vision signals are acquired from the machine using a microphone, an accelerometer and a digital CCD camera, respectively. Second, the sound and vibration signals are transformed into the frequency domain using fast Fourier transformation (FFT). A feature vector from the FFT frequency spectra is defined and extracted from the acquired information. Also, a feature based vision tracking approach—the Scale Invariant Feature Transform (SIFT)—is applied to the vision data to track the object of interest (fish) in a robust manner. Third, Sound, vibration and vision feature vectors are provided as inputs to a neuro-fuzzy network for fault detection and diagnosis. A four-layer neural network including a fuzzy hidden layer is developed in the thesis to analyze and diagnose existing faults. By training the neural network with sample data for typical faults, faults of five crucial components in the fish cutting machine are detected with high reliability and robustness. Alarms to warn about impending faults may be generated as well during the machine operation. A network-based remote monitoring architecture is developed as well in the thesis, which will facilitate engineers to monitor the machine condition in a more flexible manner from a remote site. Developed multi-sensor approaches are validated using computer simulations and physical experimentation with the industrial machine, and compared with a single-sensor approach.
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Βλάβες μετασχηματιστών από γήρανση, υπερφόρτωση και καιρικά φαινόμεναΧομπιτάκη, Μαρία 15 March 2012 (has links)
Οι μετασχηματιστές αποτελούν ένα από τα σημαντικότερα στοιχεία ενός Συστήματος Ηλεκτρικής Ενέργειας. Κατά συνέπεια, ο συνεχής έλεγχος της σωστής λειτουργίας, της απόδοσης, των καταπονήσεων και των απωλειών των μετασχηματιστών είναι ζωτικής σημασίας.
Στην παρούσα διπλωματική, ύστερα από μια εκτενή αναφορά στους μετασχηματιστές και στα βασικά χαρακτηριστικά τους, επικεντρωνόμαστε στις απώλειες, την προστασία καθώς και το διαγνωστικό έλεγχο των μεγάλων μετασχηματιστών ελαίου.
Τέλος αναφέρουμε, από τα δεδομένα και τα συμπεράσματα μας, ποια είναι η πιο συχνά χρησιμοποιούμενη διαγνωστική μέθοδος, καθώς και πως μπορεί να επιτευχθεί για μεγαλύτερη ασφάλεια και αξιοπιστία, σε συνδυασμό με χαμηλό κόστος.
Κεφάλαιο 1: Αναφέρεται, αρχικά ο ρόλος των μετασχηματιστών, για την κατανόηση του περιγράφονται οι αρχές λειτουργίας και τα κατασκευαστικά του στοιχεία. Στην συνέχεια επικεντρωνόμαστε στην περίπτωση που η γραμμή μιας εκ των τριών φάσεων τροφοδοσίας του μετασχηματιστή διακόπτεται. Αναλύουμε τα διαδοχικά βήματα προσδιορισμού των τάσεων του δευτερεύοντος τυλίγματος και κατά συνέπεια τον προσδιορισμό της φάσης του πρωτεύοντος που δεν τροφοδοτείται με τάση.
Κεφάλαιο 2: Αναφέρονται οι απώλειες και τα προβλήματα των μετασχηματιστών που προκύπτουν εξαιτίας κάποιων σφαλμάτων κατά τη λειτουργία τους ή απλά με την πάροδο των ετών. Τονίζεται η σημασία της μόνωσης των μετασχηματιστών σε σχέση με τη λειτουργική τους κατάσταση και της διάρκειας ζωής τους.
Κεφάλαιο 3: Παρατίθενται και αναλύονται οι πιο διαδεδομένες μέθοδοι για την παρακολούθηση και τη διάγνωση σφαλμάτων στους μετασχηματιστές. / Transformers are very important elements of the power grid. Consequently, the continuous observation of their proper functioning, their attribution, and their losses is vital.
In this thesis, following an extensive reference to transformers and basic features, losses, protection and diagnostic analysis of large oil transformer are emphasized. Finally, we approached, according to our data and our conclusions, the most commonly used diagnostic method, in order to achieve security and reliability in combination with low cost.
Chapter 1: firstly, we mentioned the role of transformers, in order to understand that, we described their operating principles and their component parts. Then we focused if one line of the three phases of AC power, being interrupted. We analyzed the successive steps to determine the voltages of the secondary windings and thus determine the phase of the primary windings which aren’t under voltage.
Chapter 2: there are listed the transformer's losses and problems because of the errors, during their operation or just because of the normal aging. Stressed the importance of transformers’ isolation, in relation to their functional status and the duration of their life.
Chapter 3: Listed and analyzed the most common methods for observation and fault diagnosis in transformers.
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Remote monitoring and fault diagnosis of an industrial machine through sensor fusionLang, Haoxiang 05 1900 (has links)
Fault detection and diagnosis is quite important in engineering systems, and deserves further attention in view of the increasing complexity of modern machinery. Traditional single-sensor methods of fault monitoring and diagnosis may find it difficult to meet modern industrial requirements because there is usually no direct way to measure and accurately correlate a machine fault to a single sensor output. Fusion of information from multiple sensors can overcome this shortcoming. In this thesis, a neural-fuzzy approach of multi-sensor fusion is developed for a network-enabled remote fault diagnosis system. The approach is validated by applying it to an industrial machine called the Iron Butcher, which is a machine used in the fish processing industry for the removal of the head in fish prior to further processing for canning.
An important characteristic of the fault diagnosis approach developed in this thesis is to make an accurate decision of the machine condition by fusing information from different sensors. First, sound, vibration and vision signals are acquired from the machine using a microphone, an accelerometer and a digital CCD camera, respectively. Second, the sound and vibration signals are transformed into the frequency domain using fast Fourier transformation (FFT). A feature vector from the FFT frequency spectra is defined and extracted from the acquired information. Also, a feature based vision tracking approach—the Scale Invariant Feature Transform (SIFT)—is applied to the vision data to track the object of interest (fish) in a robust manner. Third, Sound, vibration and vision feature vectors are provided as inputs to a neuro-fuzzy network for fault detection and diagnosis. A four-layer neural network including a fuzzy hidden layer is developed in the thesis to analyze and diagnose existing faults. By training the neural network with sample data for typical faults, faults of five crucial components in the fish cutting machine are detected with high reliability and robustness. Alarms to warn about impending faults may be generated as well during the machine operation. A network-based remote monitoring architecture is developed as well in the thesis, which will facilitate engineers to monitor the machine condition in a more flexible manner from a remote site. Developed multi-sensor approaches are validated using computer simulations and physical experimentation with the industrial machine, and compared with a single-sensor approach. / Applied Science, Faculty of / Mechanical Engineering, Department of / Graduate
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Analog and mixed-signal test and fault diagnosisLiu, Dong January 2003 (has links)
No description available.
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Fault Diagnosis Of AC And AC-DC Systems Using Constructive Learning RBF Neural NetworksNagabhushana, T N 12 1900 (has links) (PDF)
No description available.
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Structural Health Monitoring and Fault Diagnosis based on Artificial Immune SystemXiao, Wenchang 29 February 2012 (has links)
This thesis presents a development of Structural Health Monitoring (SHM) and Fault Diagnosis based on Artificial Immune System (AIS), a biology-inspired method motivated from the Biological Immune System (BIS). Using the antigen to model structural health or damage condition of specific characteristics and the antibody to represent an information system or a database that can identify the specific damage pattern, the AIS can detect structural damage and then take action to ensure the structural integrity. In this study the antibodies for SHM were first trained and then tested. The feature space in training includes the natural frequencies and the modal shapes extracted from the simulated structural response data including both free-vibration and seismic response data. The concepts were illustrated for a 2-DOF linear mass-spring-damper system and promising results were obtained. It has shown that the methodology can be effectively used to detect, locate, and assess damage if it occurred. Consistently good results were obtained for both feature spaces of the natural frequencies and the modal shapes extracted from both response data sets. As the only exception, some significant errors were observed in the result for the seismic response data when the second modal shape was used as the feature space. The study has shown great promises of the methodology for structural health monitoring, especially in the case when the measurement data are not sufficient. The work lays a solid foundation for future investigations on the AIS application for large-scale complex structures.
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Sensor Fault Diagnosis for Wind-driven Doubly-fed Induction GeneratorsGálvez Carrillo, Manuel Ricardo 05 January 2011 (has links)
Among the renewable energies, wind energy presents the highest growth in installed capacity and penetration in modern power systems. This is why reliability of wind turbines becomes an important topic in research and industry. To this end, condition monitoring (or health monitoring) systems are needed for wind turbines. The core of any condition monitoring system (CMS) are fault diagnosis algorithms whose task is to provide early warnings upon the occurrence of incipient (small magnitude) faults. Thanks to the use of CMS we can avoid premature breakdowns and reduce significatively maintenance costs.
The present thesis deals with fault diagnosis in sensors of a doubly-fed induction generator (DFIG) for wind turbine (WT) applications. In particular we are interested in performing fault detection and isolation (FDI) of incipient faults affecting the measurements of the three-phase signals (currents and voltages) in a controlled DFIG. Although different authors have dealt with FDI for sensors in induction machines and in DFIGs, most of them rely on the machine model with
constant parameters. However, the parameter uncertainties due to changes in the operating conditions will produce degradation in the performance of such FDI systems.
In this work we propose a systematic methodology for the design of sensor FDI systems with the following characteristics: i) capable of detecting and isolating incipient additive (bias, drifts) and multiplicative (changes in the sensor
gain) faults, ii) robust against changes in the references/disturbances affecting the controlled DFIG as well as modelling/parametric uncertainties, iii) residual generation system based on a multi-observer strategy to enhance the isolation process, iv) decision system based on statistical-change detection algorithms to treat the entire residual and perform fault detection and isolation at once.
Three novel sensor FDI approaches are proposed. The first is a signal-based approach, that uses the model of the balanced three-phase signals (currents or voltages) for residual generation purposes. The second is a model-based approach
that accounts for variation in the parameters. Finally, a third approach that combines the benefits of both the signal- and the model-based approaches is proposed. The designed sensor FDI systems have been validated using measured voltages, as well as simulated data from a controlled DFIG and a speed-controlled induction
motor.
In addition, in this work we propose a discrete-time multiple input multiple output (MIMO) regulator for each power converter, namely for the rotor side converter (RSC) and for the grid side converter (GSC). In particular, for RSC
control, we propose a modified feedback linearization technique to obtain a linear time invariant (LTI) model dynamics for the compensated DFIG. The novelty of this approach is that the compensation does not depend on highly uncertain parameters such as the rotor resistance. For GSC control, a LTI model dynamics
is derived using the ideas behind feedback linearization. The obtained LTI model dynamics are used to design Linear Quadratic Gaussian (LQG) regulators. A single design is needed for all the possible operating conditions.
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Fault monitoring in hydraulic systems using unscented Kalman filterSepasi, Mohammad 05 1900 (has links)
Condition monitoring of hydraulic systems is an area that has grown
substantially in the last few decades. This thesis presents a scheme that
automatically generates the fault symptoms by on-line processing of raw sensor data
from a real test rig. The main purposes of implementing condition monitoring in
hydraulic systems are to increase productivity, decrease maintenance costs and
increase safety. Since such systems are widely used in industry and becoming more
complex in function, reliability of the systems must be supported by an efficient
monitoring and maintenance scheme.
This work proposes an accurate state space model together with a novel
model-based fault diagnosis methodology. The test rig has been fabricated in the
Process Automation and Robotics Laboratory at UBC. First, a state space model of
the system is derived. The parameters of the model are obtained through either
experiments or direct measurements and manufacturer specifications. To validate the
model, the simulated and measured states are compared. The results show that under
normal operating conditions the simulation program and real system produce similar
state trajectories.
For the validated model, a condition monitoring scheme based on the
Unscented Kalman Filter (UKF) is developed. In simulations, both measurement and
process noises are considered. The results show that the algorithm estimates the
iii
system states with acceptable residual errors. Therefore, the structure is verified to
be employed as the fault diagnosis scheme.
Five types of faults are investigated in this thesis: loss of load, dynamic
friction load, the internal leakage between the two hydraulic cylinder chambers, and
the external leakage at either side of the actuator. Also, for each leakage scenario,
three levels of leakage are investigated in the tests. The developed UKF-based fault
monitoring scheme is tested on the practical system while different fault scenarios
are singly introduced to the system. A sinusoidal reference signal is used for the
actuator displacement. To diagnose the occurred fault in real time, three criteria,
namely residual moving average of the errors, chamber pressures, and actuator
characteristics, are considered. Based on the presented experimental results and
discussions, the proposed scheme can accurately diagnose the occurred faults.
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Fault diagnosis of a Fixed Wing UAV Using Hardware and Analytical RedundancyAndersson, Michael January 2013 (has links)
In unmanned aerial systems an autopilot controls the vehicle without human interference. Modern autopilots use an inertial navigation system, GPS, magnetometers and barometers to estimate the orientation, position, and velocity of the aircraft. In order to make correct decisions the autopilot must rely on correct information from the sensors. Fault diagnosis can be used to detect possible faults in the technical system when they occur. One way to perform fault diagnosis is model based diagnosis, where observations of the system are compared with a mathematical model of the system. Model based diagnosis is a common technique in many technical applications since it does not require any additional hardware. Another way to perform fault diagnosis is hardware diagnosis, which can be performed if there exists hardware redundancy, i.e. a set of identical sensors measuring the same quantity in the system. The main contribution of this master thesis is a model based diagnosis system for a fixed wing UAV autopilot. The diagnosis system can detect faults in all sensors on the autopilot and isolate faults in vital sensors as the GPS, magnetometer, and barometers. This thesis also provides a hardware diagnosis system based on the redundancy obtained with three autopilots on a single airframe. The use of several autopilots introduces hardware redundancy in the system, since every autopilot has its own set of sensors. The hardware diagnosis system handles faults in the sensors and actuators on the autopilots with full isolability, but demands additional hardware in the UAV.
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Simulation-based fault propagation analysis of process industry using process variable interaction analysisHosseini, Amir Hossein 01 January 2013 (has links)
There are increasing safety concerns in chemical and petrochemical process industry. The huge explosion of Nowruz oil Field platform that happened in Persian gulf-IRAN at 1983, along with other disastrous events have effected chemical industrial renaissance and led to high demand to enhance safety. Oil and chemical Industries involve complex processes and handle hazardous materials that may potentially cause catastrophic consequences in terms of human losses, injuries, asset lost and environmental stresses. One main reason of such catastrophic events is the lack of effective control and monitoring approaches that are required to achieve successful fault diagnosis and accurate hazard identification. Currently, there are aggressive worldwide efforts to propose an effective, robust, and high accuracy fault propagation analysis and monitoring techniques to prevent undesired events at early stages prior to their occurrence. Among these requirements is the development of an intelligent and automated control and monitoring system to first diagnose faulty equipment and process variable deviations, and then identify hazards associated with faults and deviations. Research into safety and control issues become high priority in all aspects. To support these needs, predictive control and intelligent monitoring system is under study and development at the Energy Safety and Control Laboratory (ESCL) – University of Ontario Institute of Technology (UOIT). The purpose of this research is to present a real time fault propagation analysis method for chemical / petrochemical process industry through fault semantic network (FSN) using accurate process variable interactions (PV-PV interactions). The effectiveness, feasibility, and robustness of the proposed method are demonstrated on simulated data emanating from a well-known Tennessee Eastman (TE) chemical process. Unlike most existing probabilistic approaches, fault propagation analysis module classifies faults and identifies faulty equipment and deviations according to obtained data from the underlying processes. It is an expert system that identifies corresponding causes and
consequences and links them together. FSN is an integrated framework that is used to link fault propagation scenarios qualitatively and quantitatively. Probability and fuzzy rules are used for reasoning causes and consequences and tuning FSN. / UOIT
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