Industrial time series modelling by means of the neo-fuzzy neuron

Abstract—Industrial process monitoring and modelling represents a critical step in order to achieve the paradigm of Zero Defect Manufacturing. The aim of this paper is to introduce the Neo-Fuzzy Neuron method to be applied in industrial time series modelling. Its open structure and input independency provides fast learning and convergence capabilities, while assuring a proper accuracy and generalization in the modelled output. First, the auxiliary signals in the database are analyzed in order to find correlations with the target signal. Second, the Neo-Fuzzy Neuron is configured and trained according by means of the auxiliary signal, past instants and dynamics information of the target signal. The proposed method is validated by means of real data from a Spanish copper rod industrial plant, in which a critical signal regarding copper refrigeration process is modelled. The obtained results indicate the suitability of the Neo-Fuzzy Neuron method for industrial process modelling.Postprint (published version

Vector machine techniques for modeling of seismic liquefaction data

AbstractThis article employs three soft computing techniques, Support Vector Machine (SVM); Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM), for prediction of liquefaction susceptibility of soil. SVM and LSSVM are based on the structural risk minimization (SRM) principle which seeks to minimize an upper bound of the generalization error consisting of the sum of the training error and a confidence interval. RVM is a sparse Bayesian kernel machine. SVM, LSSVM and RVM have been used as classification tools. The developed SVM, LSSVM and RVM give equations for prediction of liquefaction susceptibility of soil. A comparative study has been carried out between the developed SVM, LSSVM and RVM models. The results from this article indicate that the developed SVM gives the best performance for prediction of liquefaction susceptibility of soil

Vibration-based Fault Diagnostics in Wind Turbine Gearboxes Using Machine Learning

A significantly increased production of wind energy offers a path to achieve the goals of green energy policies in the United States and other countries. However, failures in wind turbines and specifically their gearboxes are higher due to their operation in unpredictable wind conditions that result in downtime and losses. Early detection of faults in wind turbines will greatly increase their reliability and commercial feasibility. Recently, data-driven fault diagnosis techniques based on deep learning have gained significant attention due to their powerful feature learning capabilities. Nonetheless, diagnosing faults in wind turbines operating under varying conditions poses a major challenge. Signal components unrelated to faults and high levels of noise obscure the signature generated by early-stage damage. To address this issue, we propose an innovative fault diagnosis framework that utilizes deep learning and leverages cyclostationary analysis of sensor data. By generating cyclic spectral coherence maps from the sensor data, we can emphasize fault-related signatures. These 2D color map representations are then used to train convolutional neural networks capable of detecting even minor faults and early-stage damages. The proposed method is evaluated using test data obtained from multibody dynamic simulations conducted under various operating conditions. The benchmark test cases, inspired by an NREL study, are successfully detected using our approach. To further enhance the accuracy of the model, subsequent studies employ Convolutional Neural Networks with Local Interpretable Model-Agnostic Explanations (LIME). This approach aids in interpreting classifier predictions and developing an interpretable classifier by focusing on a subset range of cyclic spectral coherence maps that carry the unique fault signatures. This improvement contributes to better accuracy, especially in scenarios involving multiple faults in the gearbox that need to be identified. Moreover, to address the challenge of applying this framework in practical settings, where standard deep learning techniques tend to provide inaccurate predictions for unseen faults or unusual operating conditions, we investigate fault diagnostics using a Bayesian convolutional neural network. This approach incorporates uncertainty bounds into prediction results, reducing overconfident misclassifications. The results demonstrate the effectiveness of the Bayesian approach in fault diagnosis, offering valuable implications for condition monitoring in other rotating machinery applications

In-Space Propulsion Technology Products Ready for Infusion on NASA's Future Science Missions

Since 2001, the In-Space Propulsion Technology (ISPT) program has been developing and delivering in-space propulsion technologies that will enable or enhance NASA robotic science missions. These in-space propulsion technologies are applicable, and potentially enabling, for future NASA flagship and sample return missions currently being considered. They have a broad applicability to future competed mission solicitations. The high-temperature Advanced Material Bipropellant Rocket (AMBR) engine, providing higher performance for lower cost, was completed in 2009. Two other ISPT technologies are nearing completion of their technology development phase: 1) NASA s Evolutionary Xenon Thruster (NEXT) ion propulsion system, a 0.6-7 kW throttle-able gridded ion system; and 2) Aerocapture technology development with investments in a family of thermal protection system (TPS) materials and structures; guidance, navigation, and control (GN&C) models of blunt-body rigid aeroshells; aerothermal effect models; and atmospheric models for Earth, Titan, Mars and Venus. This paper provides status of the technology development, applicability, and availability of in-space propulsion technologies that have recently completed their technology development and will be ready for infusion into NASA s Discovery, New Frontiers, SMD Flagship, or technology demonstration missions

Grid-based semantic integration of heterogeneous data resources: Implementation on a HealthGrid

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.The semantic integration of geographically distributed and heterogeneous data resources still remains a key challenge in Grid infrastructures. Today's mainstream Grid technologies hold the promise to meet this challenge in a systematic manner, making data applications more scalable and manageable. The thesis conducts a thorough investigation of the problem, the state of the art, and the related technologies, and proposes an Architecture for Semantic Integration of Data Sources (ASIDS) addressing the semantic heterogeneity issue. It defines a simple mechanism for the interoperability of heterogeneous data sources in order to extract or discover information regardless of their different semantics. The constituent technologies of this architecture include Globus Toolkit (GT4) and OGSA-DAI (Open Grid Service Architecture Data Integration and Access) alongside other web services technologies such as XML (Extensive Markup Language). To show this, the ASIDS architecture was implemented and tested in a realistic setting by building an exemplar application prototype on a HealthGrid (pilot implementation). The study followed an empirical research methodology and was informed by extensive literature surveys and a critical analysis of the relevant technologies and their synergies. The two literature reviews, together with the analysis of the technology background, have provided a good overview of the current Grid and HealthGrid landscape, produced some valuable taxonomies, explored new paths by integrating technologies, and more importantly illuminated the problem and guided the research process towards a promising solution. Yet the primary contribution of this research is an approach that uses contemporary Grid technologies for integrating heterogeneous data resources that have semantically different. data fields (attributes). It has been practically demonstrated (using a prototype HealthGrid) that discovery in semantically integrated distributed data sources can be feasible by using mainstream Grid technologies, which have been shown to have some Significant advantages over non-Grid based approaches

Condition Monitoring System of Wind Turbine Generators

The development and implementation of the condition monitoring systems (CMS) play a significant role in overcoming the number of failures in the wind turbine generators that result from the harsh operation conditions, such as over temperature, particularly when turbines are deployed offshore. In order to increase the reliability of the wind energy industry, monitoring the operation conditions of wind generators is essential to detect the immediate faults rapidly and perform appropriate preventative maintenance. CMS helps to avoid failures, decrease the potential shutdowns while running, reduce the maintenance and operation costs and maintain wind turbines protected. The knowledge of wind turbine generators\u27 faults, such as stator and rotor inter-turn faults, is indispensable to perform the condition monitoring accurately, and assist with maintenance decision making. Many techniques are utilized to avoid the occurrence of failures in wind turbine generators. The majority of the previous techniques that are applied to monitor the wind generator conditions are based on electrical and mechanical concepts and theories. An advanced CMS can be implemented by using a variety of different techniques and methods to confirm the validity of the obtained electrical and mechanical condition monitoring algorithms. This thesis is focused on applying CMS on wind generators due to high temperature by contributing the statistical, thermal, mathematical, and reliability analyses, and mechanical concepts with the electrical methodology, instead of analyzing the electrical signal and frequencies trends only. The newly developed algorithms can be compared with previous condition monitoring methods, which use the electrical approach in order to establish their advantages and limitations. For example, the hazard reliability techniques of wind generators based on CMS are applied to develop a proper maintenance strategy, which aims to extend the system life-time and reduce the potential failures during operation due to high generator temperatures. In addition, the use of some advanced statistical techniques, such as regression models, is proposed to perform a CMS on wind generators. Further, the mechanical and thermal characteristics are employed to diagnose the faults that can occur in wind generators. The rate of change in the generator temperature with respect to the induced electrical torque; for instance is considered as an indicator to the occurrence of faults in the generators. The behavior of the driving torque of the rotating permanent magnet with respect to the permanent magnet temperature can also utilize to indicate the operation condition. The permanent magnet model describes the rotating permanent magnet condition during operation in the normal and abnormal situations. In this context, a set of partial differential equations is devolved for the characterization of the rotations of the permanent. Finally, heat transfer analysis and fluid mechanics methods are employed to develop a suitable CMS on the wind generators by analyzing the operation conditions of the generator\u27s heat exchanger. The proposed methods applied based on real data of different wind turbines, and the obtained results were very convincing

MACHINERY ANOMALY DETECTION UNDER INDETERMINATE OPERATING CONDITIONS

Anomaly detection is a critical task in system health monitoring. Current practice of anomaly detection in machinery systems is still unsatisfactory. One issue is with the use of features. Some features are insensitive to the change of health, and some are redundant with each other. These insensitive and redundant features in the data mislead the detection. Another issue is from the influence of operating conditions, where a change in operating conditions can be mistakenly detected as an anomalous state of the system. Operating conditions are usually changing, and they may not be readily identified. They contribute to false positive detection either from non-predictive features driven by operating conditions, or from influencing predictive features. This dissertation contributes to the reduction of false detection by developing methods to select predictive features and use them to span a space for anomaly detection under indeterminate operating conditions. Available feature selection methods fail to provide consistent results when some features are correlated. A method was developed in this dissertation to explore the correlation structure of features and group correlated features into the same clusters. A representative feature from each cluster is selected to form a non-correlated set of features, where an optimized subset of predictive features is selected. After feature selection, the influence of operating conditions through non-predictive variables are removed. To remove the influence on predictive features, a clustering-based anomaly detection method is developed. Observations are collected when the system is healthy, and these observations are grouped into clusters corresponding to the states of operating conditions with automatic estimation of clustering parameters. Anomalies are detected if the test data are not members of the clusters. Correct partitioning of clusters is an open challenge due to the lack of research on the clustering of the machinery health monitoring data. This dissertation uses unimodality of the data as a criterion for clustering validation, and a unimodality-based clustering method is developed. Methods of this dissertation were evaluated by simulated data, benchmark data, experimental study and field data. These methods provide consistent results and outperform representatives of available methods. Although the focus of this dissertation is on the application of machinery systems, the methods developed in this dissertation can be adapted for other application scenarios for anomaly detection, feature selection, and clustering

A Dynamic Neural Network Architecture with immunology Inspired Optimization for Weather Data Forecasting

Recurrent neural networks are dynamical systems that provide for memory capabilities to recall past behaviour, which is necessary in the prediction of time series. In this paper, a novel neural network architecture inspired by the immune algorithm is presented and used in the forecasting of naturally occurring signals, including weather big data signals. Big Data Analysis is a major research frontier, which attracts extensive attention from academia, industry and government, particularly in the context of handling issues related to complex dynamics due to changing weather conditions. Recently, extensive deployment of IoT, sensors, and ambient intelligence systems led to an exponential growth of data in the climate domain. In this study, we concentrate on the analysis of big weather data by using the Dynamic Self Organized Neural Network Inspired by the Immune Algorithm. The learning strategy of the network focuses on the local properties of the signal using a self-organised hidden layer inspired by the immune algorithm, while the recurrent links of the network aim at recalling previously observed signal patterns. The proposed network exhibits improved performance when compared to the feedforward multilayer neural network and state-of-the-art recurrent networks, e.g., the Elman and the Jordan networks. Three non-linear and non-stationary weather signals are used in our experiments. Firstly, the signals are transformed into stationary, followed by 5-steps ahead prediction. Improvements in the prediction results are observed with respect to the mean value of the error (RMS) and the signal to noise ratio (SNR), however to the expense of additional computational complexity, due to presence of recurrent links