5 research outputs found
Fault Localization Models in Debugging
Debugging is considered as a rigorous but important feature of software
engineering process. Since more than a decade, the software engineering
research community is exploring different techniques for removal of faults from
programs but it is quite difficult to overcome all the faults of software
programs. Thus, it is still remains as a real challenge for software debugging
and maintenance community. In this paper, we briefly introduced software
anomalies and faults classification and then explained different fault
localization models using theory of diagnosis. Furthermore, we compared and
contrasted between value based and dependencies based models in accordance with
different real misbehaviours and presented some insight information for the
debugging process. Moreover, we discussed the results of both models and
manifested the shortcomings as well as advantages of these models in terms of
debugging and maintenance.Comment: 58-6
On the identifiability, parameter identification and fault diagnosis of induction machines
PhD ThesisDue to their reliability and low cost, induction machines have been widely utilized in a large
variety of industrial applications. Although these machines are rugged and reliable, they are
subjected to various stresses that might result in some unavoidable parameter changes and
modes of failures. A common practice in induction machine parameter identification and fault
diagnosis techniques is to employ a machine model and use the external measurements of
voltage, current, speed, and/or torque in model solution. With this approach, it might be possible
to get an infinite number of mathematical solutions representing the machine parameters,
depending on the employed machine model. It is therefore crucial to investigate such possibility
of obtaining incorrect parameter sets, i.e. to test the identifiability of the model before being
used for parameter identification and fault diagnosis purposes. This project focuses on the
identifiability of induction machine models and their use in parameter identification and fault
diagnosis.
Two commonly used steady-states induction machine models namely T-model and inverse Γ-
model have been considered in this thesis. The classical transfer function and bond graph
identifiability analysis approaches, which have been previously employed for the T-model, are
applied in this thesis to investigate the identifiability of the inverse Γ-model. A novel algorithm,
the Alternating Conditional Expectation, is employed here for the first time to study the
identifiability of both the T- and inverse Γ-models of the induction machine. The results
obtained from the proposed algorithm show that the parameters of the commonly utilised Tmodel
are non-identifiable while those of the inverse Γ-model are uniquely identifiable when
using external measurements. The identifiability analysis results are experimentally verified by
the particle swarm optimization and Levenberg-Marquardt model-based parameter
identification approaches developed in this thesis.
To overcome the non-identifiability problem of the T-model, a new technique for induction
machine parameter estimation from external measurements based on a combination of the
induction machine’s T- and inverse Γ-models is proposed. Results for both supply-fed and
inverter-fed operations show the success of the technique in identifying the parameters of the
machine using only readily available measurements of steady-state machine current, voltage
and speed, without the need for extra hardware.
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A diagnosis scheme to detect stator winding faults in induction machines is also proposed in
this thesis. The scheme uses time domain features derived from 3-phase stator currents in
conjunction with particle swarm optimization algorithm to check characteristic parameters of
the machine and detect the fault accordingly. The validity and effectiveness of the proposed
technique has been evaluated for different common faults including interturn short-circuit,
stator winding asymmetry (increased resistance in one or more stator phases) and combined
faults, i.e. a mixture of stator winding asymmetry and interturn short-circuit. Results show the
accuracy of the proposed technique and it is ability to detect the presence of the fault and
provide information about its type and location.
Extensive simulations using Matlab/SIMULINK and experimental tests have been carried out
to verify the identifiability analysis and show the effectiveness of the proposed parameter
identification and fault diagnoses schemes. The constructed test rig includes a 1.1 kW threephase
test induction machine coupled to a dynamometer loading unit and driven by a variable
frequency inverter that allows operation at different speeds. All the experiment analyses
provided in the thesis are based on terminal voltages, stator currents and rotor speed that are
usually measured and used in machine control.Libya, through the Engineering Faculty of Misurata-
Misurata Universit