13-07-2012, 05:01 PM
ELECTROMAGNETIC FLUX MONITORING FOR DETECTING FAULTS IN ELECTRICAL MACHINES
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INTRODUCTION
Background and importance of fault diagnosis and condition monitoring
Electrical machines have been used extensively for many different industrial applications
since several decades ago. These applications range from intensive care unit pumps, electric
vehicle propulsion systems, and computer-cooling fans to electric pumps used in nuclear
power plants. The electrical energy that is consumed in (induction) motors accounts for
around 60% of the electrical energy that is consumed by industry in developed economies
(Williamson 2004).
The present-day requirement for the ever-increasing reliability of electrical machines is now
more important than ever before and continues to grow. Advances are continually being made
in this area as a result of the consistent demand from the power generation and transportation
industries. Because of the progress made in engineering and materials science, rotating
machinery is becoming both faster and lighter, as well as being required to run for longer
periods of time. All of these factors mean that the detection, location, and analysis of faults
play a vital role in the good operation of the electrical machine and are essential for major
concerns such as the safety, reliability, efficiency, and performance of applications involving
electrical machines. Although continual improvement in design and manufacturing has
become a priority task among contemporary manufacturers of electrical machines, faults still
can and do occur.
Since the analysis and design of rotating machinery is critical in terms of the cost of both
production and maintenance, it is not surprising that the fault diagnosis of rotating machinery
is a crucial aspect of the subject and is receiving ever more attention. As the design of rotating
machinery becomes increasingly complex, as a result of the rapid progress being made in
technology, so machinery condition monitoring strategies must become more advanced in
order to cope with the physical burdens being placed on the individual components of a
machine.
When faults do occur and the machine fails in service, the result could, at best, be the loss of
production and revenue, or, at worst, catastrophic for the industrial process and potentially
dangerous to the operators.
The issues of preventive maintenance, on-line motor fault detection, and condition monitoring
are of increasing importance, taking into consideration essential concerns such as:
- ageing motors,
- lack of redundancy in the event of a machine failure,
- high-reliability requirements,
- cost competitiveness.
During the past twenty years, there has been a substantial amount of research into the creation
of new condition monitoring techniques for electrical machine drives, with new methods
being developed and implemented in commercial products for this purpose (Chow 2000,
Benbouzid 1999, Nandi et al. 2005). The research and development of newer and alternative
diagnostic techniques is continuous, however, since condition monitoring and fault diagnosis
systems should always suit new, specific electric motor drive applications. This continuous
research and development is also supported by the fact that no specific system/technique may
be considered generally the best for all the applications that exist, since an operator must treat
each motor drive as a unique entity. In this respect, the potential failure modes, fundamental
causes, mechanical load characteristics, and operational conditions all have to be carefully
taken into consideration when a monitoring system is to be designed or selected for a specific
application (Thomson 1999).
The large amount of previous work carried out in the area of fault diagnosis and condition
monitoring shows that there have been many challenges and opportunities for engineers and
researchers to focus on. Various recommendations and solutions concerning condition
monitoring technologies have been given in this area, mainly depending on the machine type,
size, operating conditions (loading), available instrumentation, cost constraints etc.
In order to allow analysts to correlate different aspects of each technology to troubleshoot
symptoms and determine a course of action to avert failures, several fields of science and
technology, such as electrical, mechanical, thermal, and sometimes chemical engineering
should be closely considered and combined whenever possible. This is also a stringent
requirement when aiming to build a competitive condition monitoring system.
Aim of the work
The main aim of this thesis is to study the ability of electromagnetic flux to provide useful
information about various faults in an induction machine. The usefulness of this monitoring
parameter will be assessed in comparison with some other electrical parameters used for fault
detection, such as stator phase current and circulating currents between the parallel branches
of the stator winding (if there are such).
Another aim of this thesis is to validate by experiments, when and where possible, the
accuracy of different fault signatures issuing from numerical electromagnetic field
simulations.
On the exclusive basis of data obtained from simulations, a study of the modifications brought
by various stator winding designs to some of the asymmetrical air-gap electromagnetic flux
density harmonics responsible for the detection of various faults will be carried out.
The analysis of a core fault (insulation fault in the stator lamination) artificially implemented
in a numerical electromagnetic model of a machine, in terms of finding a suitable parameter
to sense such a fault, is also studied in this work.
The area of interest for this thesis is restricted to induction machines but, the possibility of
extrapolating the findings to other machine types is discussed at various points.
Scientific contribution
Scientific contribution of the author
First of all, this study represents a detailed analysis of the electromagnetic flux patterns that
are supposed to provide potential useful information about a fault in an induction machine.
For capturing such patterns, six search coils were employed in the measurements and four
search coils in the simulations. The complexity of this sensor network for capturing the
electromagnetic flux in various parts of the electrical machine and the critical comparative
analysis of the indications provided by various fault indicators may be viewed as an important
contribution to the existing state of the art in this research area.
The investigation carried out on finding the ability of electromagnetic flux eccentricity
harmonics of the order “p±1” to detect machine abnormalities other than various types of
eccentricity, both from measurements and simulations, is considered a new contribution.
This study also presents an attempt to implement in numerical simulations an insulation fault
located in the stator lamination and to suggest an electrical fault indicator that may be
confidently used to detect such an abnormality.
The investigations of the modifications brought by various stator winding designs to: 1) some
of the asymmetrical air-gap electromagnetic flux density harmonics responsible for the
detection of various faults, and 2) to the ability of stator branch currents and, also, circulating
currents to sense such faults are considered to be further original contributions.
Contribution of other members of the research team
Most of the present research work was carried out as a part of a collaborative research project
between the Laboratory of Electromechanics and Laboratory of Control Engineering of
Helsinki University of Technology. The research project was entitled “Fault detection and
diagnosis for AC electrical machines”. The industrial partners involved in this project were
ABB Industry Oy, KCI Konecranes International Oyj, KONE Oyj, and Kuppari Mittaus Oy.
The main task of the Laboratory of Electromechanics was to develop simulation models and
to carry out measurements for electrical machines working under both faulty and healthy
conditions. Relying on the data provided by the Laboratory of Electromechanics, the main
task of the Control Engineering Laboratory was to develop advanced signal processing
techniques for fault diagnosis in electrical machines.