Student Number : 9800233A -
MSc dissertation -
School of Electrical and Information Engineering -
Faculty of Engineering and the Built Environment / Condition monitoring of machinery has increased in importance as more engineering processes
are automated and the manpower required to operate and supervise plants is reduced. The
monitoring of the condition of machinery can significantly reduce the cost of maintenance.
Firstly, it can allow an early detection of potential catastrophic fault, which could be extremely
expensive to repair. Secondly, it allows the implementation of conditions based maintenance
rather than periodic or failure based maintenance [1]. In these cases, significant savings can be
made by delaying schedule maintenance until convenient or necessary.
Although there are numerous efficient methods for modeling of mechanical systems, they all
suffer the disadvantage that they are only valid for a particular machine. Changes within the
design or the operational mode of the machine normally require a manual adaptation. Using
Neural Networks to model technical systems eliminates this major disadvantage. The basis for a
successful model is an adequate knowledge base on which the network is "trained". Without
prior knowledge of the machines systematic behavior or its history, training of a neural Network
is not possible. Therefore, it is a pre-requisite that the knowledge base contains a complete
behavior of the machine covering the respective operational modes whereby, not all rather the
most important modes are required. Neural networks have a proven ability in the area of
nonlinear pattern classification. After being trained, they contain expert knowledge and can
correctly identify the different causes of bearing vibration. The capacity of artificial neural
networks to mimic and automate human expertise is what makes them ideally suited for handling
nonlinear systems. Neural networks are able to learn expert knowledge by being trained using a
representative set of data [2]-[6]. At the beginning of a neural network’s training session, the
neural network fault detector’s diagnosis of the motor’s condition will not be accurate. An error
quantity is measured and used to adjust the neural network’s internal parameters in order to
produce a more accurate output. This process is repeated until a suitable error is achieved. Once
the network is sufficiently trained and the parameters have been saved, the neural network
contains all the necessary knowledge to perform the fault detection.
One of the most important aspects of achieving good neural network performance has proven to
be the proper selection of training features. The curse of dimensionality states that, as a rule of
thumb, the required cardinality of the training set for accurate training increases exponentially
with the input dimension [7]. Thus feature selection which is a process of identifying those
features that contribute most to the discrimination ability of the neural network is required.
Proposed methods for selecting an appropriate subset of features are numerous [8]-[11]. Methods
based on generating a single solution, such as the popular forward step wise approach, can fail to
select features which do poorly alone but offer valuable information together. Approaches that
maintain a population of solutions, such as genetic algorithms (GA) are more likely to speedily
perform efficient searches in high dimensional spaces, with strong interdependencies among the
features. The emphasis in using the genetic algorithm for feature selection is to reduce the
computational load on the training system while still allowing near optimal results to be found
relatively quickly.
To obtain accurate measure of the condition of machinery, a wide range of approaches can be
employed to select features indicative of condition. By comparing these features with features for
known normal and probable fault conditions, the machine’s condition can be estimated. The
most common approach is that of analysis in the frequency domain by applying a Fast Fourier
Transform (FFT) to the time domain history data. The idea is simply to measure the energy
(mean square value) of the vibrations. As the machine condition deteriorates, this measure is
expected to increase. The method is able to reveal the harmonics around the fundamental
frequency of the machine and other predominant frequency component (such as the cage
frequency) [12]. Frequency analysis is well established and may be used to detect, diagnose and
discriminate a variety of induction motor faults such as broken rotor bars, cage faults, phase
imbalance, inner and outer race faults. However, as common in the monitoring of any industrial
machine, background noise in recorded data can make spectra difficult to interpret. In addition,
the accuracy of a spectrum is limited due to energy leakage [12- 14].
Like many of the new techniques now finding application in machinery condition monitoring,
Higher Order Statistics was originally confined to the realms of non-linear structural dynamics. It
has of recent however found successful application to the identification of abnormal operation of diesel engines and helicopter gearboxes [5, 7]. Higher Order Statistics provide convenient basis
for comparison of data between different measurement instances and are sufficiently robust for
on-line use. They are fast in computation compared with frequency or time-domain analysis.
Furthermore, they give a more robust assessment than lower orders and can be used to calculate
higher order spectra. This dissertation reports work which attempts to extend this capability to
induction motors.
The aim of this project is therefore to examine the use of Genetic Algorithms to select the most
significant input features from a large set of possible features in machine condition monitoring
contexts. The results show the effectiveness of the selected features from the acquired raw and
preprocessed signals in diagnosis of machine condition. This project consists of the following
tasks:
#1; Using Fast Fourier transform and higher order signals techniques to preprocess data
samples.
#1; Create an intelligent engine using computational intelligence methods. The aim of
this engine will be to recognize faulty bearings and assess the fault severity from
sensor data.
#1; Train the neural network using a back propagation algorithm.
#1; Implement a feature selection algorithm using genetic algorithms to minimize the
number of selected features and to maximize the performance of the neural network.
#1; Retrain the neural network with the reduced set of features from genetic algorithm
and compare the two approaches.
#1; Investigate the effect of increasing the number of hidden nodes in the performance of
the computational intelligence engine.
#1; Evaluate the performance of the system using confusion matrices.
The output of the design is the estimate of fault type and its severity, quantified on a scale
between 0-3. Where, 0 corresponds to the absence of the specific fault and 3 the presence of a
severe machine bearing fault. This research should make contribution to many sectors of industry
such as electricity supply companies, and the railroad industry due to their need of techniques
that are capable of accurately recognizing the development of a fault condition within a machine system component. Quality control of electric motors is an essential part of the manufacturing
process as competition increases, the need for reliable and economical quality control becomes
even more pressing. To this effect, this research project will contribute in the area of faults
detection in the production line of electric motor.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/2127 |
| Date | 26 February 2007 |
| Creators | Hippolyte, Djonon Tsague |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 292848 bytes, application/pdf |
Page generated in 0.0033 seconds