An Autoassociative Neural Network Research Articles

Satellite On-Board Change Detection via Auto-Associative Neural Networks

The increase in remote sensing satellite imagery with high spatial and temporal resolutions has enabled the development of a wide variety of applications for Earth observation and monitoring. At the same time, it requires new techniques that are able to manage the amount of data stored and transmitted to the ground. Advanced techniques for on-board data processing answer this problem, offering the possibility to select only the data of interest for a specific application or to extract specific information from data. However, the computational resources that exist on-board are limited compared to the ground segment availability. Alternatively, in applications such as change detection, only images containing changes are useful and worth being stored and sent to the ground. In this paper, we propose a change detection scheme that could be run on-board. It relies on a feature-based representation of the acquired images which is obtained by means of an auto-associative neural network (AANN). Once the AANN is trained, the dissimilarity between two images is evaluated in terms of the extracted features. This information can be subsequently turned into a change detection result. This study, which presents one of the first techniques for on-board change detection, yielded encouraging results on a set of Sentinel-2 images, even in light of comparison with a benchmark technique.

FPGA-Embedded Anomaly Detection System for Milling Process

The main goal of this work is to design a supervising controller able to detect an anomaly in the milling process and implement the soultion in Field Programmable Gate Array (FPGA) chip. Executing this task, the controller continuously monitors the vibration signal coming from the acceleration sensor, installed on the milling machine, and striving to isolate new vibration patterns which are different from typical patterns recorded for the correct milling process. The detection method relies on determining selected signal features in the frequency domain and applying an auto-associative neural network (AANN) for novelty detection. It has been shown that by exercising the frequency spectrum of the vibration signal, extracting specific features of the signal’s spectrum, and using an auto-associative neural network, it is possible to detect anomalies in a milling process with relatively high efficiency. The accuracy, sensitivity, specificity, precision, and false alarm rate are equal to 94.3, 100, 91.2, 88.9, and 8.8 percent. All necessary calculations can be accomplished by the developed single-chip FPGA embedded supervising controller. The controller allows high-speed calculations under low power consumption. It characterizes high reliability and low price compared to typically encountered solutions.

Sensor data validation and fault diagnosis using Auto-Associative Neural Network for HVAC systems

The Heating, Ventilation, and Air conditioning (HVAC) system is a major system in buildings for conditioning the indoor environment. Sensor data validation and fault diagnosis for HVAC systems are essentially important to secure a reliable and efficient operation since sensor measurements are vital for the HVAC closed-loop control system. The aim of this work is to address this matter by developing a data-driven approach using the system's normal operation data and without the need for the knowledge of the mathematical model of the system. It is based on an Auto-Associative Neural Network (AANN) that is structured and trained to construct an input-output mapping model based on data dimensionality reduction that is capable of validating sensor measurements in terms of sensor error correction, missing data replacement, noise filtering, and inaccuracy correction. It can be used for both single and multiple sensor faults diagnosis by monitoring the consistency between the actual and the AANN-estimated sensor reading. The validation of the proposed method is demonstrated on data obtained from a 3-zone HVAC system simulated in TRNSYS. The evaluation results show the effectiveness of the proposed approach and an improvement in terms of data validation and diagnostic accuracy when compared with a PCA-based method.

Gas path fault diagnostics using a hybrid intelligent method for industrial gas turbine engines

There are many challenges against an accurate gas turbine fault diagnostics, such as the nonlinearity of the engine health, the measurement uncertainty, and the occurrence of simultaneous faults. The conventional methods have limitations in effectively handling these challenges. In this paper, a hybrid intelligent technique is devised by integrating an autoassociative neural network (AANN), nested machine learning (ML) classifiers, and a multilayer perceptron (MLP). The AANN module is used as a data preprocessor to reduce measurement noise and extract the important features for visualisation and fault diagnostics. The features are extracted from the bottleneck layer output values based on the concept of the nonlinear principal component analysis (NLPCA). The nested classifier modules are then used in such a manner that fault and no-fault conditions, component and sensor faults, and different component faults are distinguished hierarchically. As part of the classification, evaluation of the fault classification performance of five widely used ML techniques aiming to identify alternative approaches is undertaken. In the end, the MLP approximator is utilised to estimate the magnitude of the isolated component faults in terms of flow capacity and isentropic efficiency indices. The developed system was implemented to diagnose up to three simultaneous faults in a two-shaft industrial gas turbine engine. Its robustness towards the measurement uncertainty was also evaluated based on Gaussian noise corrupted data. The test results show the derivable benefits of integrating two or more methods in engine diagnostics on the basis of offsetting the weakness of the one with the strength of another.

Nonlinear PCA for Visible and Thermal Hyperspectral Images Quality Enhancement

In this letter, we propose a method aiming at reducing the noise in hyperspectral images based on the nonlinear generalization of principal component analysis (NLPCA). NLPCA is performed by an autoassociative neural network (AANN) that has the hyperspectral image as input and is trained to reconstruct the same image at the output. Due to its topology, characterized by a bottleneck layer, the nonlinear AANN forces the hyperspectral image to be projected in a lower dimensionality feature space by removing noise and both linear and nonlinear correlations between spectral bands. This process permits to obtain enhancements in terms of the quality of the reconstructed hyperspectral image. The results conducted on different hyperspectral images are qualitatively and quantitatively discussed and demonstrate the potentialities of the proposed method, as compared with similar approaches such as PCA and kernel PCA.

On damage diagnosis for a wind turbine blade using pattern recognition

With the increased interest in implementation of wind turbine power plants in remote areas, structural health monitoring (SHM) will be one of the key cards in the efficient establishment of wind turbines in the energy arena. Detection of blade damage at an early stage is a critical problem, as blade failure can lead to a catastrophic outcome for the entire wind turbine system. Experimental measurements from vibration analysis were extracted from a 9 m CX-100 blade by researchers at Los Alamos National Laboratory (LANL) throughout a full-scale fatigue test conducted at the National Renewable Energy Laboratory (NREL) and National Wind Technology Center (NWTC). The blade was harmonically excited at its first natural frequency using a Universal Resonant EXcitation (UREX) system. In the current study, machine learning algorithms based on Artificial Neural Networks (ANNs), including an Auto-Associative Neural Network (AANN) based on a standard ANN form and a novel approach to auto-association with Radial Basis Functions (RBFs) networks are used, which are optimised for fast and efficient runs. This paper introduces such pattern recognition methods into the wind energy field and attempts to address the effectiveness of such methods by combining vibration response data with novelty detection techniques.

Fault Diagnosis of Electrical Power Systems Using Soft Computing

Monitoring of electric power systems is particularly challenging due to the presence of dynamic load changes of network nodes, as well as the presence of both continuous and discrete variables, noisy information and lack of data. The need to develop more powerful approaches has been recognized, and hybrid techniques that combine several reasoning methods start to be used. A fault diagnosis framework that is able to locate the set of nodes involved in multiple fault events is proposed. A methodology based on the system history data combining an AutoAssociative Neural Network (AANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) detects faulty nodes, type of faults and time when the faults appear.

Significance of image representation for face verification

In this paper we discuss the significance of representation of images for face verification. We consider three different representations, namely, edge gradient, edge orientation and potential field derived from the edge gradient. These representations are examined in the context of face verification using a specific type of correlation filter, called the minimum average correlation energy (MACE) filter. The different representations are derived using one-dimensional (1-D) processing of image. The 1-D processing provides multiple partial evidences for a given face image, one evidence for each direction of the 1-D processing. Separate MACE filters are used for deriving each partial evidence. We propose a method to combine the partial evidences obtained for each representation using an auto-associative neural network (AANN) model, to arrive at a decision for face verification. Results show that the performance of the system using potential field representation is better than that using the edge gradient representation or the edge orientation representation. Also, the potential field representation derived from the edge gradient is observed to be less sensitive to variation in illumination compared to the gray level representation of images.

The autoassociative neural network in signal analysis: III. Enhancing the reliability of a NN with application to a BWR

This is the third and last of a series of papers trying to unveil the opaqueness of neural networks structure through a geometrical approach [Marseguerra M., Zoia, A., 2005a. The autoassociative neural network in signal analysis: I. The data dimensionality reduction and its geometric interpretation. Ann. Nucl. Energy 32, 1191–1206, Marseguerra, M., Zoia, A., 2005b. The autoassociative neural network in signal analysis: II. Application to on-line monitoring of a simulated BWR component. Ann. Nucl. Energy 32, 1207–1223]. Artificial neural networks (NN) provide a powerful tool in the operation of complex systems, such as nuclear power plants, in that they are suitable to determine the relationship between measured variables and control parameters on the basis of input-output examples. However, their major drawback is the fact that they always provide an output to the user, regardless of the appropriateness of the input. In this paper, we propose to adopt an autoassociative neural network (AANN) to work in cooperation with the NN to first assess the well-posedness of the desired neural model and to successively establish the appropriateness of the input data. The neural algorithm has been applied to a nuclear problem: the estimation of the reactivity forcing function parameters from the values of the measured neutron flux in a BWR reactor (provided by a reduced-order literature model). In this example, the AANN was able to suggest through geometrical considerations how to decompose the dataset in order to obtain a successful training for the NN and thereafter to validate the input data, thus enhancing the reliability of the NN model output.

06/00938 The dynamic thermal characteristics of lightweight building elements with a forced ventilated cavity and radiation barriers: Cerne, B. and Medved, S. Energy and Buildings, 2005, 37, (9), 972–981.

A new procedure for classifying bioprocess performance using artificial neural networks is presented. Using historical records of processes with known good and bad outcomes as reference sets, patterns in the data are represented as data clusters generated from the bottleneck values of an autoassociative neural network (AANN). The strategy is similar to analyzing the cluster of points generated when plotting the first and second principal components. This method departs, however, from principal component analysis (PCA) in that it can handle data clouds generated from more than 2 variables and the classification is assessed quantitatively as opposed to visually. This is achieved by feeding the cloud patterns into a feed-forward network which compares the sample’s data clouds to reference sets. Using a classification scheme based on Soft Independent Modeling of Class Analogy (SIMCA), the net is able to classify a process as good, bad, neither, or both as early as possible.

The autoassociative neural network in signal analysis: I. The data dimensionality reduction and its geometric interpretation

In complex and risky plants, such as the nuclear reactors, the analysis of the signals released by the many sensors which monitor the plant represents a difficult task due to the high-dimensionality of the data. This paper is the first of two in which we tackle the problem of the dimensionality reduction by the nonlinear principal components analysis as performed by an autoassociative neural network (AANN). This network filters the many input data and releases at the bottleneck output a relatively small number of signals which capture the significant properties of the original data, thus realizing the data reduction. In the present paper, we show that the network ability in correctly reproducing as output the given input after a passage through the bottleneck layer (which by definition should have fewer nodes than either input or output layers) could be conceived as a topological mapping between abstract spaces. Apart from the less critical choice of the number of nodes in the mapping and demapping layers, the topological mapping will be successful – and the AANN will be able to perform the required data reconstruction – provided that the number of nodes of the bottleneck layer is related to the dimensionality d of the abstract projection space. We show how to obtain a numerical estimate d* for the real dimension d. This numerical estimate will firmly base the choice of the number of nodes f of the bottleneck layer, thus avoiding the usual troubling trial-and-error procedure. The power of the proposed approach is demonstrated firstly on a few geometrical cases and then on the analysis of nuclear transients simulated by the classic Chernick’s model.

Thermal power estimation by fouling phenomena compensation using wavelet and principal component analysis

A small percentage of reactor thermal power can be overestimated because of fouling phenomena in a secondary feedwater flowmeter. This study proposes a signal processing technique for the compensation of a degraded flowmeter such a secondary feedwater flowmeter in nuclear power plants. The technique proposed is mainly focused on noise classification and step-by-step noise reduction. The noises focused are classified into the rapid distortion caused by environmental interference, the flow fluctuation according to plant state transition and the degradation by fouling phenomena qualitatively. The multi-step de-noising technique reduces each noise by three techniques step-by-step. The wavelet analysis as a low frequency pass filter to remove the rapid distortion, the linear principal component analysis (PCA) to predict a steady-state value from the fluctuation, and the non-linear PCA implemented as an autoassociative neural network (AANN) to predict an original value from the signal including fouling phenomena are developed. The main purpose of this approach is to make an AANN concentrate on compensating the degradation by fouling phenomena itself. For the demonstration the signals from a simulator and signal modeling were used so that the role and the performance of each noise removal step was represented. In addition a thermal power deviation estimator is proposed to recognize the degradation effect of each operating parameter for reactor thermal power calculation.

Quantification of Data Clusters for Bioprocess Performance Classification via Artificial Neural Networks

A new procedure for classifying bioprocess performance using artificial neural networks is presented. Using historical records of processes with known good and bad outcomes as reference sets, patterns in the data are represented as data clusters generated from the bottleneck values of an autoassociative neural network (AANN). The strategy is similar to analyzing the cluster of points generated when plotting the first and second principal components. This method departs, however, from principal component analysis (PCA) in that it can handle data clouds generated from more than 2 variables and the classification is assessed quantitatively as opposed to visually. This is achieved by feeding the cloud patterns into a feed-forward network which compares the sample’s data clouds to reference sets. Using a classification scheme based on Soft Independent Modeling of Class Analogy (SIMCA), the net is able to classify a process as good, bad, neither, or both as early as possible.

On-line fault diagnosis for optimal rice α-amylase production process of a temperature-sensitive mutant of Saccharomyces cerevisiae by an autoassociative neural network

A nonlinear statistical approach to data analysis, an autoassociative neural network (AANN), was applied to fault diagnosis in the optimal production process of a recombinant yeast with a temperature controllable expression system. High frequency noise in the data could be eliminated by a wavelet transform before the fault diagnosis was performed. The diagnosis system could accurately and immediately detect the faults on-line in the test cases of a faulty temperature sensor and plasmid instability of the recombinant cells. The same faults were not detected by linear principal component analysis (PCA). By implementing corrective action after fault detection, the final production amount was increased to twice the amount it would have been without diagnosis.

Auditing incidents of exposure to blood

<h3>Abstract</h3> This paper presents a new conceptual and computational dynamics framework for damage detection and regeneration in multicellular structures similar to living animals. The model uniquely achieves complete and accurate regeneration from any damage anywhere in the system. We demonstrated the efficacy of the proposed framework on an artificial organism consisting of three tissue structures corresponding to the head, body and tail of a worm. Each structure consists of a stem cell surrounded by a tissue of differentiated cells. We represent a tissue as an Auto-Associative Neural Network (AANN) with local interactions and stem cells as a self-repair network with long-range interactions. We also propose another new concept, Information Field which is a mathematical abstraction over traditional components of tissues, to keep minimum pattern information of the tissue structures to be accessed by stem cells in extreme cases of damage. Through entropy, a measure of communication between a stem cell and differentiated cells, stem cells monitor the tissue pattern integrity, violation of which triggers damage detection and tissue repair. Stem cell network monitors its state and invokes stem cell repair in the case of stem cell damage. The model accomplishes regeneration at two levels: In the first level, damaged tissues with intact stem cells regenerate themselves. Here, stem cell identifies entropy change and finds the damage and regenerates the tissue in collaboration with the AANN. In the second level, involving missing whole tissues and stem cells, the remaining stem cell(s) access the information field to restore the stem cell network and regenerate missing tissues. In the case of partial tissue damage with missing stem cells, the two levels collaborate to accurately restore the stem cell network and tissues. This comprehensive hypothetical framework offers a new way to conceptualise regeneration for better understanding the regeneration processes in living systems. It could also be useful in biology for regenerative medicine and in engineering for building self-repairing biobots.