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2 edition of Diverse neural net solutions to a fault diagnosis problem found in the catalog.

Diverse neural net solutions to a fault diagnosis problem

Amanda J. C. Sharkey

Diverse neural net solutions to a fault diagnosis problem

by Amanda J. C. Sharkey

  • 236 Want to read
  • 24 Currently reading

Published by University of Sheffield, Dept. of Computer Science in Sheffield .
Written in English


Edition Notes

StatementAmanda J.C. Sharkey, Noel E. Sharkey and Gopinath O. Chandroth.
SeriesMemoranda in computer and cognitive science -- CS-95-10
ContributionsSharkey, N. E., Chandroth, Gopinath O., University of Sheffield. Department of Computer Science.
ID Numbers
Open LibraryOL17273835M

  On the other hand, making neural nets “deep” results in unstable gradients. This can be divided into two parts, namely the vanishing and the exploding gradient problems. The weights of a neural network are generally initialised with random values, having a mean 0 and standard deviation 1, placed roughly on a Gaussian distribution. Fault Identification Using Neural Networks And Vibration Data A dissertation submitted to the University of Cambridge for the degree of Doctor of Philosophy.

The collected data from industrial machines are often imbalanced, which poses a negative effect on learning algorithms. However, this problem becomes more challenging for a mixed type of data or while there is overlapping between classes. Class- imbalance problem requires a robust learning system which can timely predict and classify the data. We propose a new adversarial network for. We consider the fault identification problem, also known as the system-level self-diagnosis, in multiprocessor and multicomputer systems using the comparison approach. In this diagnosis model, a set of tasks is assigned to pairs of nodes and their outcomes are compared by neighboring nodes. Given that comparisons are performed by the nodes themselves, faulty nodes can incorrectly claim that.

Amato et al.: Artificial neural networks in medical diagnosis Fig. 2. General structure of a neural network with two hidden layers. The w ij is the weight of the connection between the i-th and the j-th node. (ii) transforming the net j through a suitable mathe-matical “transfer function”, and (iii) transferring the result to neurons in the. A look at a specific application using neural networks technology will illustrate how it can be applied to solve real-world problems. An interesting example can be found at the University of Saskatchewan, where researchers are using MATLAB and the Neural Network Toolbox to determine whether a popcorn kernel will pop.. Knowing that nothing is worse than a half-popped bag of popcorn, they set.


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Diverse neural net solutions to a fault diagnosis problem by Amanda J. C. Sharkey Download PDF EPUB FB2

The development of a neural net system for fault diagnosis in a marine diesel engine is described. Nets were trained to classify combustion quality on the basis of simulated data. Three different types of data were used: pressure, temperature and.

The development of a neural net system for fault diagnosis in a marine diesel engine is described. Nets were trained to classify combustion quality on the basis of simulated data. Three different types of data were used: pressure, temperature and combined pressure and temperature.

Subsequent to training, three nets were selected and combined by means of a majority voter to form a system which Cited by: Diverse neural net solutions to a fault diagnosis problem Article (PDF Available) in Neural Computing and Applications 4(4) January with 43 Reads How we measure 'reads'.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The development of a neural net system for fault diagnosis in a marine diesel engine is described. Nets were trained to classify combustion quality on the basis of simulated data.

Three different types of data were used; Pressure, Temperature and Combined Pressure and Temperature. Neural networks have strengths for modeling systems and solving classification problems like fault detection & diagnosis, especially in nonlinear systems.

[Kramer & Leonard, ]. The neural net learns the plant model based solely on the data. This approach has limitations, however, when. NEURAL NETWORKS FOR FAULT DIAGNOSIS BASED ON MODEL ERRORS OR DATA RECONCILIATION Greg M.

Stanley* - Gensym Corporation Presented at: ISA 93 (Instrument Society of America), Chicago, IL, USA, Sept. This paper proposes a neural‐network‐based methodology for providing a potential solution to the preceding problems in the area of process fault diagnosis. The potential of this approach is demonstrated with the aid of an oil refinery case study of the fluidized catalytic cracking process.

An appropriate use of neural computing techniques is to apply them to problems such as condition monitoring, fault diagnosis, control and sensing, where conventional solutions can be hard to obtain.

However, when neural computing techniques are used, it is important that they are employed so as to maximise their performance, and improve their. A new convolutional neural network based data-driven fault diagnosis method.

IEEE Transactions on Industrial Electronics, Vol. 65, Issue 7,p. Jia F., Lei Y. G., Lu N. Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization.

Mechanical Systems and. Railway turnout system is a key infrastructure to railway safety and efficiency. However, it is prone to failure in the field.

Therefore, many railway departments have adopted a monitoring system to monitor the operation status of turnouts. With monitoring data collected, many researchers have proposed different fault-diagnosis methods.

However, many of the existing methods cannot realize real. The book presents the application of neural networks to the modelling and fault diagnosis of industrial processes. The first two chapters focus on the funda-mental issues such as the basic definitions and fault diagnosis schemes as well as a survey on ways of using neural networks in different fault diagnosis strategies.

The use of artificial neural networks (ANN) in fault detection analysis is widespread. This paper aims to provide an overview on its application in the field of fault identification and diagnosis (FID), as well as the guiding elements behind their successful implementations in engineering-related applications.

In most of the reviewed studies, the ANN architecture of choice for FID problem. This paper presents a neural-network-based approach for the problem of sensor failure detection, identification, and accommodation for a flight control system without physical: redundancy in the.

methods, artificial neural network has good robustness and strong learning ability. It can find laws from large data. Neural network technology has become the research hotspot in the field of analog circuit fault diagnosis.

Researchers have presented some diagnosis methods based on neural network. Abstract: Fault detection and diagnosis is an important problem in process automation. Both model-based methods and expert systems have been suggested to solve the problem, along with the pattern recognition approach.

A number of possible neural network architectures for fault diagnosis. fault condition, gases from different faults are mixed up re-sulting in confusing ratio between different gas components. To deal with this problem, ANN technique has been used since the relationships between the fault types and dissolved gases can be recognized by Artificial Neural Network through a training process [1].

2 D ISSOLVED G AS A. BP neural network: The artificial neural networks are widely used in pattern recognition and other fields and the most used one is BP neutral network (Wu et al., ) which is a multilayer feed forward network and can be used in fault diagnosis fields.

Usually, the action function of neurons mostly is Sigmoid function. The purpose of the paper is to develop an efficient approach to fault-tolerant control for nonlinear systems of magnetic brakes.

The challenging problems of accurate modeling, reliable fault detection and a control design able to compensate for potential sensor faults are addressed. The main idea here is to make use of the repetitive character of the control task and apply iterative learning.

The fault f 3, that is, an incipient fault of group 1, is simulated during the time interval [ s s]; then f 15, that is, an abrupt fault of group 2, is simulated during the time interval [ s s]; finally f 5, that is, an incipient fault, is simulated during time interval [.

This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods.

Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the. The real-time fault diagnosis system is very important for steam turbine generator set due serious fault re-sults in a reduced amount of electricity supply in power plant.

A novel real-time fault diagnosis system is proposed by using Levenberg-Marquardt algorithm related to tuning parameters of Artificial Neural Network .These outputs have a clear numerical relationship; e.g.

the output of 6 feet is twice the output of 3 feet. This type of problem is known as a regression problem.

Artificial neural networks (ANNs) are flexible enough to be used in both classification and regression problems. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis.

Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features.