Spelling suggestions: "subject:"faultdetection"" "subject:"multidetection""
71 |
A multi-agent Based Fault Location Detection of Distribution Network with Distributed GenerationsWang, Chin-hsien 24 July 2009 (has links)
In current distribution automations design, fault flags generated by overcurrent relays are used to detect the feeder fault section. With the integration of distributed generations (DG), fault currents could be contributed from different directions and jeopardize the fault detection function. A large fault current contributed by a DG flows from downstream of a feeder could be detected by the overcurrent relay and lead to the confusion in fault detection function. In this thesis, adjunction current measurements and fault flags are utilized to minimize the possibility of mis-identification of fault section. The structure and data flow of a Java agent development framework (JADE) is adopted for feeder fault detection, identification and service restoration (FDIR). Based on information from local measurements and other agents, the FDIR function can be better conducted by local agents. Test results indicate that multi-agent systems can be used to improve system reliability and reduce service interruption time.
|
72 |
Fault detection and precedent-free localization in thermal-fluid systemsCarpenter, Katherine Patricia 16 February 2011 (has links)
This thesis presents a method for fault detection and precedent-free isolation for two types of channel flow systems, which were modeled with the finite element method. Unlike previous fault detection methods, this method requires no a priori knowledge or training pertaining to any particular fault. The basis for anomaly detection was the model of normal behavior obtained using the recently introduced Growing Structure Multiple Model System (GSMMS). Anomalous behavior is then detected as statistically significant departures of the current modeling residuals away from the modeling residuals corresponding to the normal system behavior. Distributed anomaly detection facilitated by multiple anomaly detectors monitoring various parts of the thermal-fluid system enabled localization of anomalous partitions of the system without the need to train classifiers to recognize an underlying fault. / text
|
73 |
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.
|
74 |
Fault diagnosis of sampled data systemsMostafavi, Somayeh Unknown Date
No description available.
|
75 |
Data-Driven Fault Detection, Isolation and Identification of Rotating Machinery: with Applications to Pumps and GearboxesZhao, Xiaomin Unknown Date
No description available.
|
76 |
State and Parameter Estimation in LPV SystemsWang, Ying Unknown Date
No description available.
|
77 |
Distributed fault detection and diagnostics using artificial intelligence techniques / A. LucouwLucouw, Alexander January 2009 (has links)
With the advancement of automated control systems in the past few years, the focus
has also been moved to safer, more reliable systems with less harmful effects on the
environment. With increased job mobility, less experienced operators could cause more
damage by incorrect identification and handling of plant faults, often causing faults to
progress to failures. The development of an automated fault detection and diagnostic
system can reduce the number of failures by assisting the operator in making correct
decisions. By providing information such as fault type, fault severity, fault location
and cause of the fault, it is possible to do scheduled maintenance of small faults rather
than unscheduled maintenance of large faults.
Different fault detection and diagnostic systems have been researched and the best
system chosen for implementation as a distributed fault detection and diagnostic
architecture. The aim of the research is to develop a distributed fault detection and
diagnostic system. Smaller building blocks are used instead of a single system that
attempts to detect and diagnose all the faults in the plant.
The phases that the research follows includes an in-depth literature study followed by
the creation of a simplified fault detection and diagnostic system. When all the aspects
concerning the simple model are identified and addressed, an advanced fault detection
and diagnostic system is created followed by an implementation of the fault detection
and diagnostic system on a physical system. / Thesis (M.Ing. (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2009.
|
78 |
Model based fault detection for two-dimensional systemsWang, Zhenheng 05 May 2014 (has links)
Fault detection and isolation (FDI) are essential in ensuring safe and reliable operations in industrial
systems. Extensive research has been carried out on FDI for one dimensional (1-D)
systems, where variables vary only with time. The existing FDI strategies are mainly focussed
on 1-D systems and can generally be classified as model based and process history data based
methods. In many industrial systems, the state variables change with space and time (e.g., sheet
forming, fixed bed reactors, and furnaces). These systems are termed as distributed parameter
systems (DPS) or two dimensional (2-D) systems. 2-D systems have been commonly represented
by the Roesser Model and the F-M model. Fault detection and isolation for 2-D systems
represent a great challenge in both theoretical development and applications and only limited
research results are available.
In this thesis, model based fault detection strategies for 2-D systems have been investigated
based on the F-M and the Roesser models. A dead-beat observer based fault detection has been
available for the F-M model. In this work, an observer based fault detection strategy is investigated
for systems modelled by the Roesser model. Using the 2-D polynomial matrix technique,
a dead-beat observer is developed and the state estimate from the observer is then input to a
residual generator to monitor occurrence of faults. An enhanced realization technique is combined
to achieve efficient fault detection with reduced computations. Simulation results indicate
that the proposed method is effective in detecting faults for systems without disturbances as well
as those affected by unknown disturbances.The dead-beat observer based fault detection has been shown to be effective for 2-D systems
but strict conditions are required in order for an observer and a residual generator to exist. These
strict conditions may not be satisfied for some systems. The effect of process noises are also not
considered in the observer based fault detection approaches for 2-D systems. To overcome the
disadvantages, 2-D Kalman filter based fault detection algorithms are proposed in the thesis. A recursive 2-D Kalman filter is applied to obtain state estimate minimizing the estimation
error variances. Based on the state estimate from the Kalman filter, a residual is generated
reflecting fault information. A model is formulated for the relation of the residual with faults
over a moving evaluation window. Simulations are performed on two F-M models and results
indicate that faults can be detected effectively and efficiently using the Kalman filter based fault
detection.
In the observer based and Kalman filter based fault detection approaches, the residual signals
are used to determine whether a fault occurs. For systems with complicated fault information
and/or noises, it is necessary to evaluate the residual signals using statistical techniques. Fault
detection of 2-D systems is proposed with the residuals evaluated using dynamic principal component
analysis (DPCA). Based on historical data, the reference residuals are first generated using
either the observer or the Kalman filter based approach. Based on the residual time-lagged
data matrices for the reference data, the principal components are calculated and the threshold
value obtained. In online applications, the T2 value of the residual signals are compared with
the threshold value to determine fault occurrence. Simulation results show that applying DPCA
to evaluation of 2-D residuals is effective.
|
79 |
Distributed fault detection and diagnostics using artificial intelligence techniques / A. LucouwLucouw, Alexander January 2009 (has links)
With the advancement of automated control systems in the past few years, the focus
has also been moved to safer, more reliable systems with less harmful effects on the
environment. With increased job mobility, less experienced operators could cause more
damage by incorrect identification and handling of plant faults, often causing faults to
progress to failures. The development of an automated fault detection and diagnostic
system can reduce the number of failures by assisting the operator in making correct
decisions. By providing information such as fault type, fault severity, fault location
and cause of the fault, it is possible to do scheduled maintenance of small faults rather
than unscheduled maintenance of large faults.
Different fault detection and diagnostic systems have been researched and the best
system chosen for implementation as a distributed fault detection and diagnostic
architecture. The aim of the research is to develop a distributed fault detection and
diagnostic system. Smaller building blocks are used instead of a single system that
attempts to detect and diagnose all the faults in the plant.
The phases that the research follows includes an in-depth literature study followed by
the creation of a simplified fault detection and diagnostic system. When all the aspects
concerning the simple model are identified and addressed, an advanced fault detection
and diagnostic system is created followed by an implementation of the fault detection
and diagnostic system on a physical system. / Thesis (M.Ing. (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2009.
|
80 |
Soft Sensors for Process Monitoring of Complex ProcessesSerpas, Mitchell Roy 2012 August 1900 (has links)
Soft sensors are an essential component of process systems engineering schemes. While soft sensor design research is important, investigation into the relationships between soft sensors and other areas of advanced monitoring and control is crucial as well. This dissertation presents two new techniques that enhance the performance of fault detection and sensor network design by integration with soft sensor technology. In addition, a chapter is devoted to the investigation of the proper implementation of one of the most often used soft sensors. The performance advantages of these techniques are illustrated with several cases studies.
First, a new approach for fault detection which involves soft sensors for process monitoring is developed. The methodology presented here deals directly with the state estimates that need to be monitored. The advantage of such an approach is that the nonlinear effect of abnormal process conditions on the state variables can be directly observed. The presented technique involves a general framework for using soft sensor design and computation of the statistics that represent normal operating conditions.
Second, a method for determining the optimal placement of multiple sensors for processes described by a class of nonlinear dynamic systems is described. This approach is based upon maximizing a criterion, i.e., the determinant, applied to the empirical observability gramian in order to optimize certain properties of the process state estimates. The determinant directly accounts for redundancy of information, however, the resulting optimization problem is nontrivial to solve as it is a mixed integer nonlinear programming problem. This paper also presents a decomposition of the optimization problem such that the formulated sensor placement problem can be solved quickly and accurately on a desktop PC.
Many comparative studies, often based upon simulation results, between Extended Kalman filters (EKF) and other estimation methodologies such as Moving Horizon Estimation or Unscented Kalman Filter have been published over the last few years. However, the results returned by the EKF are affected by the algorithm used for its implementation and some implementations may lead to inaccurate results. In order to address this point, this work provides a comparison of several different algorithms for implementation.
|
Page generated in 0.072 seconds