164 research outputs found
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
Cutting tool condition monitoring of the turning process using artificial intelligence
This thesis relates to the application of Artificial Intelligence to tool wear monitoring. The
main objective is to develop an intelligent condition monitoring system able to detect when a
cutting tool is worn out. To accomplish this objective it is proposed to use a combined Expert
System and Neural Network able to process data coming from external sensors and combine
this with information from the knowledge base and thereafter estimate the wear state of the
tool.
The novelty of this work is mainly associatedw ith the configurationo f the proposeds ystem.W ith
the combination of sensor-baseidn formation and inferencer ules, the result is an on-line system
that can learn from experience and can update the knowledge base pertaining to information
associated with different cutting conditions. Two neural networks resolve the problem of
interpreting the complex sensor inputs while the Expert System, keeping track of previous
successe, stimatesw hich of the two neuraln etworks is more reliable. Also, mis-classificationsa re
filtered out through the use of a rough but approximate estimator, the Taylor's tool life equation.
In this study an on-line tool wear monitoring system for turning processesh as been developed
which can reliably estimate the tool wear under common workshop conditions. The system's
modular structurem akesi t easyt o updatea s requiredb y different machinesa nd/or processesT. he
use of Taylor's tool life equation, although weak as a tool life estimator, proved to be crucial in
achieving higher performance levels. The application of the Self Organizing Map to tool wear
monitoring is, in itself, new and proved to be slightly more reliable then the Adaptive Resonance
Theory neural network
VHDL-AMS modeling of self-organizing neural systems
This paper presents VHDL-AMS models and simulation results for a complex, self-organizing neural system based on the adaptive resonance theory. Such neural systems exhibit both discrete and continuous dynamic behavior and consist of a large number of analog equations, a digital controller with analog and digital feedback paths resulting in the complexity that would prohibit analysis with conventional mixed-signal simulation tools
Evaluation of Manufactured Product Performance Using Neural Networks
This paper discusses some of the several successful applications of neural networks which have made them a useful simulation tool. After several years of neglect, confidence in the accuracy of neural networks began to grow from the 1980s with applications in power, control and instrumentation and robotics to mention a few. Several successful industrial implementations of neural networks in the field of electrical engineering will be reviewed and results of the authors’ research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%
Exploratory Cluster Analysis from Ubiquitous Data Streams using Self-Organizing Maps
This thesis addresses the use of Self-Organizing Maps (SOM) for exploratory cluster
analysis over ubiquitous data streams, where two complementary problems arise:
first, to generate (local) SOM models over potentially unbounded multi-dimensional
non-stationary data streams; second, to extrapolate these capabilities to ubiquitous environments.
Towards this problematic, original contributions are made in terms of algorithms
and methodologies. Two different methods are proposed regarding the first
problem. By focusing on visual knowledge discovery, these methods fill an existing gap
in the panorama of current methods for cluster analysis over data streams. Moreover,
the original SOM capabilities in performing both clustering of observations and features
are transposed to data streams, characterizing these contributions as versatile compared to existing methods, which target an individual clustering problem. Also, additional methodologies that tackle the ubiquitous aspect of data streams are proposed in respect to the second problem, allowing distributed and collaborative learning strategies.
Experimental evaluations attest the effectiveness of the proposed methods and realworld applications are exemplified, namely regarding electric consumption data, air quality monitoring networks and financial data, motivating their practical use.
This research study is the first to clearly address the use of the SOM towards ubiquitous data streams and opens several other research opportunities in the future
Coordinated Machine Learning and Decision Support for Situation Awareness
For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator\u27s input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario
Learning manipulative skills using an artificial intelligence approach.
The aim of this research was to design a non-linear controller based on an Artificial Neural Network and Reinforcement Learning algorithms implementation, which is able to perform an intelligent robotic assembly of mechanical components. Different information was applied and combined to develop a fully unsupervised, intelligent controller. In the author's design no class labelling or geometry feature pretraining takes place. Only force and torque signals together with the direction of insertion were supplied to the controller. A unique sandwich structure of the intelligent controller was proposed. It featured two major layers, a State Recognition module where the detection and localisation of the contact points were performed, and the Decision Making subsystem where the decision about the next action took place.All the algorithms were implemented and tested on simulated data before being applied to the real-life peg-in-hole insertion. The results are presented in the form of graphs and tables.Evaluation of the environmental uncertainty was accomplished. The signal from the force and torque sensor was acquired under controlled conditions. All the data was collected to establish the area and level of uncertainty (e.g. signal errors) the artificial controller would need to learn to cope with and compensate for.The empirical part of the thesis includes the investigation into the effects of different learning methods applied on the same geometry. The influence of action-selection methods on AI agent performance was analysed. The proposed controller was applied to a set of real life peg-and-hole experiments. Both circular and square peg geometries were used, and insertions into chamfered and non-chamfered holes were performed. Materials with different friction factors were used for mating parts.Fast and stable knowledge acquisition was clearly present in all the cases investigated. A significant reduction in contact force value during the initial stage of the learning process was recorded. The force was usually reduced to one tenth of the initial value. Some fluctuations were recorded but when the cylindrical peg was considered the value of contact forces never exceeded 0.5 N during the steady state
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