Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

Dineva, Adrienn; Mosavi, Amir; Ardabili, Sina Faizollahzadeh; Vajda, István; Shamshirband, Shahaboddin; Rabczuk, Timon; Chau, Kwok Wing

doi:10.3390/en12061049

Cited by 63 publications

(44 citation statements)

References 97 publications

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“…Reference [46] discussed multiple artificial intelligent models utilized for hydrologic model prediction in past decade. Reference [47] highlighted the opportunities and challenges presented by big data for informed decision-making. Reference [48] developed a food forecast model using multiple optimization methods.…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

“…After defining research questions, next activity was to select the sources from where the articles would be retrieved. For this purpose, we have adopted the searching strategy given in [47,48] and Sustainability 2020, 12, 2420 6 of 32 have developed a comprehensive search string based on the key terms given in Table 2. Articles have been collected from Elsevier Scopus digital library (www.scopus.com).The terms stated in Table 2 have been used to develop the search string for searching articles from the given sources.…”

Section: Search and Selection Strategymentioning

confidence: 99%

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Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

2020

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Earthquake is one of the most hazardous natural calamity. Many algorithms have been proposed for earthquake prediction using expert systems (ES). We aim to identify and compare methods, models, frameworks, and tools used to forecast earthquakes using different parameters. We have conducted a systematic mapping study based upon 70 systematically selected high quality peer reviewed research articles involving ES for earthquake prediction, published between January 2010 and January 2020.To the best of our knowledge, there is no recent study that provides a comprehensive survey of this research area. The analysis shows that most of the proposed models have attempted long term predictions about time, intensity, and location of future earthquakes. The article discusses different variants of rule-based, fuzzy, and machine learning based expert systems for earthquake prediction. Moreover, the discussion covers regional and global seismic data sets used, tools employed, to predict earth quake for different geographical regions. Bibliometric and meta-information based analysis has been performed by classifying the articles according to research type, empirical type, approach, target area, and system specific parameters. Lastly, it also presents a taxonomy of earthquake prediction approaches, and research evolution during the last decade.

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Section: Machine Learning (Ml)mentioning

confidence: 99%

Section: Search and Selection Strategymentioning

confidence: 99%

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

2020

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“…Among them, machine learning methods have been reported to deliver higher performance in terms of accuracy, robustness, and lower computational power in dealing with uncertainties and big data [38][39][40][41]. Several surveys report that ensemble and hybrid models are the future trends in machine learning due to the fact of their optimized algorithms for higher efficiency [42][43][44][45][46][47][48]. Hybrid machine learning models are shown to deliver higher performance in air pollution modeling and prediction [49][50][51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

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“…Rotating electrical machines are responsible for converting a great amount of worldwide energy into mechanical energy [1][2][3]. Mobility, transportation, logistics, construction, production, agriculture, food, automation, and basically, any economical activities and industries directly or indirectly depend on rotating electrical machines [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Dineva¹,

Mosavi²,

Gyimesi³

et al. 2019

Preprint

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Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. Performance of various multi-label classification models are compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

Cited by 63 publications

References 97 publications

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

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