Self-organizing Map Method Research Articles

An Integrated Framework of Drivetrain Degradation Assessment and Fault Localization for Offshore Wind Turbines

As wind energy proliferates in onshore and offshore applications, it has become significantly important to predict wind turbine downtime and maintain operation uptime to ensure maximal yield. Two types of data systems have been widely adopted for monitoring turbine health condition: supervisory control and data acquisition (SCADA) and condition monitoring system (CMS). Provided that research and development have focused on advancing analytical techniques based on these systems independently, an intelligent model that associates information from both systems is necessary and beneficial. In this paper, a systematic framework is designed to integrate CMS and SCADA data and assess drivetrain degradation over itslifecycle. Information reference and advanced feature extraction techniques are employed to procure heterogeneous health indicators. A pattern recognition algorithm is used to model baseline behavior and measure deviation of current behavior, where a Self-organizing Map (SOM) and minimum quantization error (MQE) method is selected to achieve degradation assessment. Eventually, the computation and ranking of component contribution to the detected degradation offers component-level fault localization. When validated and automated by various applications, the approach is able to incorporate diverse data resources and output actionable information to advise predictive maintenance with precise fault information. Theapproach is validated on a 3 MW offshore turbine, where an incipient fault is detected well before existing system shuts down the unit. A radar chart is used to illustrate the fault localization result.

Explore training self-organizing map methods for clustering high-dimensional flood inundation maps

The Self-Organizing Map (SOM) can supportively organize complex datasets such as highly dimensional flood inundation maps. Nevertheless, SOM may produce distinct patterns after being trained with identical samples or may not converge in clustering highly dimensional datasets, which causes usability concerns and prevents its applications from a broader spectrum. Motivated by such concerns, two training strategies (S1 and S2) were proposed to configure SOM based on a large number of highly dimensional flood inundation maps associated with two basins located in southern Taiwan. S1 focused mainly on the weights’ adjustments in the ordering stage, while S2 would methodically balance the ordering and convergence activities on the weights’ adjustments. The effectiveness and suitability of S1 and S2 were inspected in detail by using coverage ratio, flip detector, and five clustering indices based on their configured topological maps in the two basins. The clustering results showed that the flip detector and the coverage ratio could visibly and objectively examine the suitability of the configured topological map. It was noticed that the influences of the ordering and convergence stages upon both training strategies for building SOM could significantly affect the coverage ratio as well as flip condition. Comparing the SOM topological maps implemented separately with each strategy, S2 strategy has a lower probability of causing a flipping situation and takes far fewer iterations to train a model of the same network size, which indicates S2 is more efficient and effective than S1 in configuring the SOM topological map for representing regional flood inundation maps.

A Global Quasigeostrophic Diagnosis of Extratropical Extreme Precipitation

AbstractThis paper presents a global picture of the dynamic processes and synoptic characteristics of extratropical extreme precipitation events (EPEs), defined as annual maximum daily precipitation averaged over 7.5° × 7.5° regional boxes. Based on the quasigeostrophic omega equation, extreme precipitation can be decomposed into components forced by large-scale adiabatic disturbances and amplified by diabatic heating feedback. The spatial distribution of the diabatic feedback parameter is largely controlled by atmospheric precipitable water and captured by a simple model. Most spatial heterogeneities of EPEs in the middle and high latitudes are due to the spatial variations of large-scale adiabatic forcing. The adiabatic component includes the processes of vorticity advection, in which the synoptic vorticity advection by background wind dominates; temperature advection, in which the total meridional temperature advection by synoptic wind dominates; and boundary forcing. The synoptic patterns of EPEs in all extratropical regions can be classified into six clusters using the self-organizing map method: two clusters in low latitudes and four clusters in middle and high latitudes. Synoptic disturbances are characterized by strong pressure anomalies throughout the troposphere over the coastal regions and oceans and feature upper-level shortwave disturbances and a large westward tilt with height over land. Synoptic configurations favor moisture transport from ocean to land over coastal regions.

Dynamic multiobjective optimization for thrust allocation in ship application

Thrust allocation is a key procedure in the dynamic position system (DPS) of marine vessels. The present work aims to study the characteristics of dynamic optimization in thrust allocation. A two-phase analysis process is proposed. In phase-I, the model for thrust allocation is generated from the viewpoint of multiobjective optimization. Fuel consumption and tear-and-wear on the thrusters are chosen as objectives. Multiobjective feasibility enhanced particle swarm optimization algorithm (MOFEPSO) is applied to find the Pareto set of this problem. An additional decision-making procedure, the technique of order preference by similarity to ideal solution (TOPSIS) is utilized to choose the final compromise solution in phase-II. The self-organizing map (SOM) technique is undertaken to mine the Pareto data set. A Remote Operated Vehicle (ROV) example is provided for illustrating the above analysis process. The effects of relative importance between objectives upon characteristics of thrust allocation are studied. The trajectories of decision variables and objectives are examined through the SOM method. Results from numerical examples demonstrate that the multiobjective optimization method together with decision-making skills can extend the application of optimization in the thrust allocation field. The findings in this work add to the understanding of relationships among several aspects of DPS.

Analysis of the qualitative evolution of groundwater in the Abouabou area in the Port-Bouët municipality (Abidjan, Côte d’Ivoire)

The study aimed to analyze the seasonal qualitative evolution of the Quaternary groundwater in the Abouabou area in order to see the capacity of this water to be used as a water supply by the populations. In-situ measurements (temperature, electrical conductivity, dissolved oxygen, turbidity and pH) and chemical parameter analyses (NH4+, Ca2+, Mg2+, K+, Na+, NO3-, NO2-, PO43-, SO42-, Cl− and HCO3-) were performed on the 24 samples collected during the four (4) seasons of the year. The use of Kruskal-Wallis and ANOVA tests has allowed the monitoring of seasonal variations in hydro-chemical parameters in well and borehole water. Also, the Piper diagram permit to identify the main hydrochemical facies according to the seasons. Finally, the Kohonen Self Organizing Maps (SOM) method was applied to physico-chemical parameters in order to highlight the spatial distribution of groundwater quality in the Abouabou area. The results show that, based on the physico-chemical parameters analysed, the groundwater is of good quality due to meeting WHO standards for drinking water consumption during all seasons of the year. Significant seasonal variations were recorded in the values of the parameters like turbidity, pH and Cl− for well water and turbidity, PO43-, NO3-, NH4+, K+, Cl− for borehole water. The hydrochemical facies shows a seasonal variation. Analysis of Abouabou's water reveals the variation of its hydrochemical facies. Thus, bicarbonate, calcium and magnesium facies during the long dry and rainy seasons move towards the chlorine, calcium and magnesium facies during the short dry and rainy seasons. Four water groups have been identified using SOM method, including heterogeneous groups composed mainly of borehole water (I and III) or of well water (II and IV). Borehole and well water acquire most of their mineralization by the infiltration of surface elements. Drinking water from boreholes is of good quality.

Enrichment, sources and ecological risk mapping of heavy metals in agricultural soils of dhaka district employing SOM, PMF and GIS methods

Rapid urbanization and industrial growth have triggered heavy metal contamination in agricultural soil in Dhaka, which is a serious concern for ecological risk and public health issues. In this study, fifty-four soil samples from agricultural lands of Dhaka had been analyzed for assessing accumulation, spatial enrichment, ecological risk and sources apportionment of heavy metals using a combined approach of self-organizing map (SOM), positive matrix factorization (PMF), geographical information system (GIS), and enrichment factor (EF). The results of the enrichment factor, geoaccumulation index and contamination factor index showed that more than 90% of the soil samples were polluted by higher levels of Cr and Cd. The mean pollution load index (PLI) results demonstrated that about 73% of soil samples were moderately polluted by heavy metals. Based on SOM and PMF analysis, four potential sources of heavy metals were found in this study area: (i) agrochemical and sewage irrigation (Cd–As); (ii) combined effect of agriculture, industrial and natural sources (Mn, Co, Ni and Zn); (iii) atmospheric deposition and industrial emission (As–Pb); (iv) chemical and leather tanning industries (Cr). The ecological risk index demonstrated that in terms of Cd content, about 75% of soil samples were moderate to high risk, and 20% were moderate to considerable ecological risk, which was the serious environmental, ecological, and public health concern. The spatial projection of ecological risk values showed that the southern part of Dhaka (Keraniganj Upazila) is a high ecological risk in terms of heavy metal pollution. These risk maps in agricultural soils may play a vital role in reducing pollution sources; so that zonal pollution control, as well as ecological protection, may be achieved in this resource-based agricultural land.

Investigations into Synoptic Spatiotemporal Characteristics of Coastal Upper Ocean Circulation Using High Frequency Radar Data and Model Output

Numerical models and remote sensing observation systems such as radars are useful for providing information on surface flows for coastal areas. Evaluation of their performance and extracting synoptic characteristics are challenging and important tasks. This research aims to investigate synoptic characteristics of surface flow fields through undertaking a detailed analysis of model results and high frequency radar (HFR) data using self-organizing map (SOM) and empirical orthogonal function (EOF) analysis. A dataset of surface flow fields over thirteen days from these two sources was used. A SOM topology map of size 4 × 3 was developed to explore spatial patterns of surface flows. Additionally, comparisons of surface flow patterns between SOM and EOF analysis were carried out. Results illustrate that both SOM and EOF analysis methods are valuable tools for extracting characteristic surface current patterns. Comparisons indicated that the SOM technique displays synoptic characteristics of surface flow fields in a more detailed way than EOF analysis. Extracted synoptic surface current patterns are useful in a variety of applications, such as oil spill treatment and search and rescue. This research provides an approach to using powerful tools to diagnose ocean processes from different aspects. Moreover, it is of great significance to assess SOM as a potential forecasting tool for coastal surface currents.

Combination of Self-organizing Map and k-means Methods of Clustering for Online Games Marketing

Combination of Self-organizing Map and k-means Methods of Clustering for Online Games Marketing

Pollution zone identification research during ozone pollution processes.

Identifying an ozone pollution zone during the pollution processes is significant for ozone pollution management and environmental health risk assessment. However, few studies have focused on ozone pollution zone identification during pollution processes. A spatial-temporal clustering framework for identifying pollution zones during ozone pollution processes was initially proposed in this study, and an ozone pollution process in China in May 2017 was selected as a case. The results showed that the framework can help selecting one more accurate method to identify the pollution zone according to the pollution characteristics of air pollution process. In addition, different ozone pollution zone identification methods work well in different scenarios: The self-organizing map (SOM) method was suitable for identifying the zone with the duration of pollution between 24 and 48 h, the image fusion based on wavelet transform (IFbWT) method for the zone with the duration of pollution over 48 h and the Apriori method for the zone with obvious boundaries between high-value and low-value ozone concentrations. The proposed procedure can also be applied to identify the pollution zone of the pollution process of other pollutants.

Developing a hand sizing system for a hand exoskeleton device based on the Kansei Engineering method

This study aimed to identify the customers’ requirements for a hand rehabilitation exoskeleton device using the Kansei Engineering approach and propose improvements. The Kansei Engineering approach was used to extract the design attributes for a hand rehabilitation exoskeleton device by conducting an in-depth interview with 3 physical therapists. The Kano questionnaire survey investigated 25 participants with hand rehabilitation experience. The Kano classification results found three attractive factors and corresponded it to the design attributes of the evaluation grid method results that were suitable (size), flexible (controllability), and light (weight). The principal component analysis results indicated three components can be considered as the improvement categories; functions, structure-texture, and appearance of the hand exoskeleton device. The new functional and structural designs were proposed in a previous study. This study collects 110 male participants’ hand anthropometric data to establish a hand sizing system as a design improvement (suitable attribute). The 3D and manual measurements were obtained for 63 hand dimensions with an open palm posture. The 14 dorsal length dimensions with three bent finger postures were also manually measured. The results showed that the $${\text{MAD}}_{\text{Precision}}$$ (mean absolute difference) of 3D measurement is smaller than that of manual measurement, indicating better reproducibility of 3D scanning. The $${\text{MAD}}_{\text{Accuracy}}$$ of all dimensions are within the 1 mm of the referenced ISO 20685 criteria. Significant differences were found in the 14 dorsal hand length, showing that databases should explicitly state the aspect (dorsal or palmar) where dimensions have been of measurement. The 77 hand dimensions were taken into consideration to develop a hand sizing system using the clustering results. The two-level self-organizing map method was performed and 3 handshape types and 8 sizes were extracted. The coverage rate of the sizing system was over 85%. These results provide useful information for designing and manufacturing for hand-related products.

Analysis of sustainable consumer behavior as a business opportunity

The purpose of this paper is to analyze how consumers incorporate sustainability issues into their buying behavior. This paper applies the Self-Organizing Map method to a sample of 223 consumers who answered a questionnaire based on their current behavior rather than the way they thought they ought to behave. The results of this study indicate that consumers incorporate the dimensions of sustainability, acting in a more socially responsible manner, when they have a perception of the effectiveness of their buying behavior. The identified segments can help firms develop sustainability strategies to align their sustainable strategic goals with the needs and behavior of consumers, thereby targeting potential socially responsible customers more effectively. The main conclusion is that the manufacturers should increase transparency regarding the product manufacturing and distribution processes and include the product traceability information on the label or through some other medium.

AHP versus DEA: a comparative analysis for the gradual improvement of unsustainable suppliers

PurposeData envelopment analysis (DEA) and analytical hierarchy process (AHP) are two widely applied methods to evaluate and rank suppliers in terms of sustainability. In this study, to investigate the extent to which potential differences in the outcomes of these two methods influence the benchmarking strategies, a comparative analysis based on a common set of data gathered from 19 logistics service providers is implemented.Design/methodology/approachAs suppliers' sustainability cannot be improved in a single-step process due to several limitations, improvement needs to proceed gradually. Therefore, using the self-organising map method, the suppliers were classified into clusters within a novel framework for gradually improving their sustainability. Then, the two processes of gradual improvement based on the outcomes of DEA and AHP were compared.FindingsThe findings show that although the rankings of suppliers guided by the methods correlated to a high degree, the benchmarking strategies provided by the methods for gradually improving the sustainability of suppliers differed considerably. In particular, whereas AHP suggests a benchmarking policy better suited for unsustainable or less sustainable suppliers with limited access to resources, DEA proposes one for suppliers able to dramatically boost their sustainability with few quick, significant leaps in performance.Originality/valueFirst, this study revealed a novel gradual improvement framework using the self-organising map method. Second, it clarified the extent to which the benchmarking policies are influenced by the type of evaluation method.

Integrating artificial neural network and scoring systems to increase the prediction accuracy of patient mortality and organ dysfunction.

The aim of this study was to develop and compare techniques to increase the prediction accuracy of patient mortality and organ dysfunction in the Intensive Care Units (hereinafter ICU) of hospitals. Patient mortality was estimated with two models of artificial neural network (ANN)-backpropagation (BP) and simplified acute physiology score (SAPS). Organ dysfunction was predicted by coupled ANN self-organizing map (SOM) and logistic organ dysfunction score (LODS) method on the basis of patient conditions. Input dataset consisted of 36 features recorded for 4,000 patients in the ICU. An integrated response surface methodology (RSM) and genetic algorithm (GA) was developed to achieve the best topology of the ANN-BP model. Although mortality prediction of the best ANN-BP (MSE=0.0036, AUC=0.83, R2=0.81) was more accurate than that of the SAPS score model (MSE=0.0056, AUC=0.82, R2=0.78), the execution time of the former (=45min) was longer than that of the latter (=20min). Therefore, the principal component analysis (PCA) was used to reduce the input feature dimensions, which, in turn, reduced the execution time up to 50%. Data reduction also helped to increase the network accuracy up to 90%. The likelihood of organ dysfunction determined by coupled ANN and scoring method technique can be much more efficient than the LODS model alone because the SOM could successfully classify the patients in 64 classes. The primary patient classification plays a major role in increasing the efficiency of an estimator.

Visualization Analysis of Seismic Facies Based on Deep Embedded SOM

As a classical visualization tool for seismic facies analysis, the clustering process of self-organizing map (SOM) is generally divided into two stages: feature extraction and clustering. However, when the horizon pickings are ambiguous and waveforms are chaotic, the structural feature extraction cannot correspond well to the topological structure of SOM. To improve the performance of classification, we propose a one-stage method of deep embedded SOM (DESOM) for seismic facies visualization analysis, which means that the extracting feature representation and clustering are completed simultaneously. Moreover, the stability of the DESOM results can be improved by adding sparse constraints, and thus the visualization results can be displayed in more detail. In the experiment, by comparing with the external indexes of clustering and the hierarchical results of prototype categories, the superiorities of the DESOM and sparse DESOM (SDESOM) methods are verified based on a geophysical model. In the field data application, these methods are combined with the Hue, Saturation, Value (HSV) color mapping technology to display the geological structure information of the target horizon. According to the law of the correlation of facies and the internal cluster indexes, it approves that the DESOM method can improve the continuity of channels, and the SDESOM method can obtain more detailed information of seismic facies distribution.

A spatially explicit analysis of chronic diseases in small areas: a case study of diabetes in Santiago, Chile

BackgroundThere is a strong spatial correlation between demographics and chronic diseases in urban areas. Thus, most of the public policies aimed at improving prevention plans and optimizing the allocation of resources in health networks should be designed specifically for the socioeconomic reality of the population. One way to tackle this challenge is by exploring within a small geographical area the spatial patterns that link the sociodemographic attributes that characterize a community, its risk of suffering chronic diseases, and the accessibility of health treatment. Due to the inherent complexity of cities, soft clustering methods are recommended to find fuzzy spatial patterns. Our main motivation is to provide health planners with valuable spatial information to support decision-making. For the case study, we chose to investigate diabetes in Santiago, Chile.MethodsTo deal with spatiality, we combine two statistical techniques: spatial microsimulation and a self-organizing map (SOM). Spatial microsimulation allows spatial disaggregation of health indicators data to a small area level. In turn, SOM, unlike classical clustering methods, incorporates a learning component through neural networks, which makes it more appropriate to model complex adaptive systems, such as cities. Thus, while spatial microsimulation generates the data for the analysis, the SOM method finds the relevant socio-economic clusters. We selected age, sex, income, prevalence of diabetes, distance to public health services, and type of health insurance as input variables. We used public surveys as input data.ResultsWe found four significant spatial clusters representing 75 percent of the whole population in Santiago. Two clusters correspond to people with low educational levels, low income, high accessibility to public health services, and a high prevalence of diabetes. However, one presents a significantly higher level of diabetes than the other. The second pair of clusters is made up of people with high educational levels, high income, and low prevalence of diabetes. What differentiates both clusters is accessibility to health centers. The average distance to the health centers of one group almost doubles that of the other.ConclusionsIn this study, we combined two statistical techniques: spatial microsimulation and selforganising maps to explore the relationship between diabetes and socio-demographics in Santiago, Chile. The results have allowed us to corroborate the importance of the spatial factor in the analysis of chronic diseases as a way of suggesting differentiated solutions to spatially explicit problems. SOM turned out to be a good choice to deal with fuzzy health and socioeconomic data. The method explored and uncovered valuable spatial patterns for health decision-making. In turn, spatial microsimulation.

Monitoring Pneumatic Actuators\u2019 Behavior Using Real-World Data Set

Developing a big data signal processing method is to monitor the behavior of a common component: a pneumatic actuator. The method is aimed at supporting condition-based maintenance activities: monitoring signals over an extended period, and identifying, classifying different machine states that may indicate abnormal behavior. Furthermore, preparing a balanced data set for training supervised machine learning models that represent the component’s all identified conditions. Peak detection, garbage removal and down-sampling by interpolation were applied for signal preprocessing. Undersampling the over-represented signals, Ward’s hierarchical clustering with multivariate Euclidean distance calculation and Kohonen self-organizing map (KSOM) methods were used for identifying and grouping similar signal patterns. The study demonstrated that the behavior of equipment displaying complex signals could be monitored with the method described. Both hierarchical clustering and KSOM are suitable methods for identifying and clustering signals of different machine states that may be overlooked if screened by humans. Using the proposed methods, signals could be screened thoroughly and over a long period of time that is critical when failures or abnormal behavior is rare. Visual display of the identified clusters over time could help analyzing the deterioration of machine conditions. The clustered signals could be used to create a balanced set of training data for developing supervised machine learning models to automatically identify previously recognized machine conditions that indicate abnormal behavior.

Enhancing Linear Independent Component Analysis: Comparison of Various Metaheuristic Methods

Various methods have been exploited in the blind source separation problems, especially in cocktail party problems. The most commonly used method is the independent component analysis (ICA). Many linear and nonlinear ICA methods, such as the radial basis functions (RBF) and self-organizing map (SOM) methods utilise neural networks and genetic algorithms as optimisation methods. For the contrast function, most of the traditional methods, especially the neural networks, use the gradient descent as an objective function for the ICA method. Most of these methods trap in local minima and consume numerous computation requirements. Three metaheuristic optimisation methods, namely particle, quantum particle, and glowworm swarm optimisation methods are introduced in this study to enhance the existing ICA methods. The proposed methods exhibit better results in separation than those in the traditional methods according to the following separation quality measurements: signal-to-noise ratio, signal-to-interference ratio, log-likelihood ratio, perceptual evaluation speech quality and computation time. These methods effectively achieved an independent identical distribution condition when the sampling frequency of the signals is 8 kHz.

SOM-Based High-Dimensional Design Spaces Mapping for Multi-Objective Optimization

Multi-objective optimization can reveal the complex parameter-objective relationships in the high-dimensional design problems. However, the data-extraction and data-presentation of the high-dimensional complex nonlinear system suffers from the increasing dimensionality. Key features and data-distribution of high-dimensional design spaces:parameter and objective spaces could be obtained by using Self-Organizing Maps (SOM) method, which re-clusters the high-dimensional multi-attribute data existing on the Pareto front into several low-dimensional maps. Correlations among all the design variables can be drawn according the colorized topological structure of the maps. Under the constraints including geometric structure and operating parameters, a low-cost and high accurate Kriging surrogate model was established to optimize a hybrid sliding bearing based on the sequential design method. Correlations between 3 objectives:"friction-to-load" ratio, temperature rise, instability threshold speed and 4 design parameters were extracted by SOM. Optimal feature regions were captured and analyzed. Results show that, within the specific feasible design space, supply pressure, axial bearing land width have important impact on the selected objectives, whereas the other parameters such as deep pocket depth and shallow pocket angle have relatively limited impact. A series of corresponding design decisions and optimization results help to understand the mechanism of the hybrid sliding bearing system in a much more intuitive way.

Synoptic weather patterns for large wildfires in the northwestern United States—a climatological analysis using three classification methods

Wildfire regimes respond to atmospheric variability on multiple time scales from interannual variations of drought to daily fluctuations of humidity and wind. Synoptic weather patterns effectively link both short- and long-term atmospheric variability, and thus, understanding fire-prone synoptic patterns is an integral part of fire management programs. Different analysis procedures of varying complexity have been used to identify the most common synoptic weather patterns conducive to wildfires. In this study, we examine the utility of three synoptic-pattern classification methodologies, namely Composite, Empirical Orthogonal Function (EOF), and Self Organizing Map (SOM). We focused on the synoptic weather patterns associated with 203 wildfires that burned 50,000 acres (20,250 ha) or more and 80 wildfires that burned 100,000 acres (40,500 ha) or more in the Northwestern United States between 1984 and 2014. The basic 500-hPa geopotential height patterns identified by the three analysis methods were found to be similar for the two fire-size categories, but the strengths of the geopotential height anomalies were larger for the larger size fires. All three methods were able to consistently identify the dominant pattern with only small differences in strength and position of the anomalies, but the EOF and SOM methods yielded other less dominant, but still important, patterns. Particularly, the SOM method captured transitional synoptic patterns that may account for 20–30% of these large wildfires.

A modified SOM method based on nonlinear neural weight updating for bearing fault identification in variable speed condition

Fault identification for bearings of special electromechanical equipment is significant to avoid catastrophic accidents. However, spectral aliasing and nonstationarity resulted from variable speed condition make this task difficult. In this paper, a modified self-organizing maps (SOM) based on nonlinear neural weight updating way is proposed to solve the problem of bearing fault severity identification in variable speed condition. Firstly, a multi-domain features extraction method based on angular re-sampling technique is introduced. Then considering the nonlinear relationship between fault severity and fault features, the traditional Euclidian distance of SOM is substituted with the geodesic distance when update the neural weight and select the best-matching cell, which can improve the nonlinear identification ability of proposed method. Finally, two cases are performed and the results show that the method can identify bearing fault with different severities effectively and have practical significance when considering both accuracy and time cost compared with other methods.