Fault diagnosis of air-conditioning refrigeration system based on sparse autoencoder

Abstract To overcome the drawbacks of using supervised learning to extract fault features for classification and low nonlinearity of the features in most of current fault diagnosis of air-conditioning refrigeration system, sparse autoencoder (SAE) is presented to extract fault features that are used as the input to the classifier and to achieve fault diagnosis for air-conditioning refrigeration system. The SAE structure is tuned by adjusting the number of hidden layers and nodes to build the optimal model, which is compared with the fault diagnosis model based on support vector machine. Results indicate that the indexes of the model combined with SAE, such as accuracy, precision and recall, are all improved, especially for the faults with high complexity. Besides, SAE shows high generalization ability with small-scale sample data and high efficiency with large-scale data. Obviously, the use of SAE can effectively optimize the diagnosis performance of the classifier.

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Online teaching quality evaluation model based on support vector machine and decision tree

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189218 ◽

2020 ◽

pp. 1-11

Author(s):

Jingwen Hou

Keyword(s):

Online Teaching ◽

Large Scale ◽

Quality Evaluation ◽

Dimensional Space ◽

Teaching Quality ◽

Support Vector ◽

Small Scale ◽

Data Sets ◽

Large Scale Data ◽

Scale Data

At present, online education evaluation models are insufficient when dealing with small-scale evaluation data sets. In order to discriminate the learner’s learning state, this paper further studies online teaching machine learning methods, and introduces adaptive learning rate and momentum terms to improve the gradient descent method of BP neural network to improve the convergence rate of the model. Moreover, this study proposes a deep neural network model to deal with complex high-dimensional large-scale data set problems. In the process of supervised prediction, this study uses support vector regression as a predictor for supervised prediction, and this study maps complex non-linear relationships into high-dimensional space to achieve a linear relationship similar to low-dimensional space. In addition, in this study, small-scale teaching quality evaluation data sets and large-scale data sets are input into the model to perform experiments. Finally, the model proposed in this study is compared with other shallow models. The results show that the model proposed in this research is effective and advantageous in evaluating teaching quality in universities and processing large-scale data sets.

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Support Vector Machines in Big Data Classification: A Systematic Literature Review

10.21203/rs.3.rs-663359/v1 ◽

2021 ◽

Author(s):

Mohammad Hassan Almaspoor ◽

Ali Safaei ◽

Afshin Salajegheh ◽

Behrouz Minaei-Bidgoli

Keyword(s):

Machine Learning ◽

Big Data ◽

Large Scale ◽

Support Vector ◽

Research Areas ◽

Large Scale Data ◽

Training Samples ◽

Big Data Classification ◽

Scale Data

Abstract Classification is one of the most important and widely used issues in machine learning, the purpose of which is to create a rule for grouping data to sets of pre-existing categories is based on a set of training sets. Employed successfully in many scientific and engineering areas, the Support Vector Machine (SVM) is among the most promising methods of classification in machine learning. With the advent of big data, many of the machine learning methods have been challenged by big data characteristics. The standard SVM has been proposed for batch learning in which all data are available at the same time. The SVM has a high time complexity, i.e., increasing the number of training samples will intensify the need for computational resources and memory. Hence, many attempts have been made at SVM compatibility with online learning conditions and use of large-scale data. This paper focuses on the analysis, identification, and classification of existing methods for SVM compatibility with online conditions and large-scale data. These methods might be employed to classify big data and propose research areas for future studies. Considering its advantages, the SVM can be among the first options for compatibility with big data and classification of big data. For this purpose, appropriate techniques should be developed for data preprocessing in order to covert data into an appropriate form for learning. The existing frameworks should also be employed for parallel and distributed processes so that SVMs can be made scalable and properly online to be able to handle big data.

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Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Energies ◽

10.3390/en12173396 ◽

2019 ◽

Vol 12 (17) ◽

pp. 3396 ◽

Cited By ~ 2

Author(s):

Mingzhu Tang ◽

Wei Chen ◽

Qi Zhao ◽

Huawei Wu ◽

Wen Long ◽

...

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Support Vector Regression ◽

Confidence Intervals ◽

Wind Turbines ◽

Large Scale ◽

Support Vector ◽

Effective Response ◽

Support Vector Regression Model ◽

Doubly Fed

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

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An iterative method for classification of binary data

Information and Inference A Journal of the IMA ◽

10.1093/imaiai/iaaa003 ◽

2020 ◽

Author(s):

Denali Molitor ◽

Deanna Needell

Keyword(s):

Binary Data ◽

Large Scale ◽

Support Vector ◽

Large Scale Data ◽

Classification Framework ◽

Vector Machines ◽

Inference Methods ◽

Compressed Data ◽

Scale Data

Abstract In today’s data-driven world, storing, processing and gleaning insights from large-scale data are major challenges. Data compression is often required in order to store large amounts of high-dimensional data, and thus, efficient inference methods for analyzing compressed data are necessary. Building on a recently designed simple framework for classification using binary data, we demonstrate that one can improve classification accuracy of this approach through iterative applications whose output serves as input to the next application. As a side consequence, we show that the original framework can be used as a data preprocessing step to improve the performance of other methods, such as support vector machines. For several simple settings, we showcase the ability to obtain theoretical guarantees for the accuracy of the iterative classification method. The simplicity of the underlying classification framework makes it amenable to theoretical analysis.

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Solar Upgrading of Fuels for Generation of Electricity

Journal of Solar Energy Engineering ◽

10.1115/1.1353177 ◽

2001 ◽

Vol 123 (2) ◽

pp. 160-163 ◽

Cited By ~ 71

Author(s):

Rainer Tamme ◽

Reiner Buck ◽

Michael Epstein ◽

Uriyel Fisher ◽

Chemi Sugarmen

Keyword(s):

Gas Turbines ◽

Large Scale ◽

Fossil Fuels ◽

High Efficiency ◽

Combined Cycle ◽

Test Facility ◽

Small Scale ◽

Start Up ◽

Efficiency Conversion ◽

Solar Field

This paper presents a novel process comprising solar upgrading of hydrocarbons by steam reforming in solar specific receiver-reactors and utilizing the upgraded, hydrogen-rich fuel in high efficiency conversion systems, such as gas turbines or fuel cells. In comparison to conventionally heated processes about 30% of fuel can be saved with respect to the same specific output. Such processes can be used in small scale as a stand-alone system for off-grid markets as well as in large scale to be operated in connection with conventional combined-cycle plants. The complete reforming process will be demonstrated in the SOLASYS project, supported by the European Commission in the JOULE/THERMIE framework. The project has been started in June 1998. The SOLASYS plant is designed for 300 kWel output, it consists of the solar field, the solar reformer and a gas turbine, adjusted to operate with the reformed gas. The SOLASYS plant will be operated at the experimental solar test facility of the Weizmann Institute of Science in Israel. Start-up of the pilot plant is scheduled in April 2001. The midterm goal is to replace fossil fuels by renewable or non-conventional feedstock in order to increase the share of renewable energy and to establish processes with only minor or no CO2 emission. Examples might be upgrading of bio-gas from municipal solid waste as well as upgrading of weak gas resources.

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Small-scale microwave background anisotropies implied by large-scale data

The Astrophysical Journal ◽

10.1086/172140 ◽

1993 ◽

Vol 402 ◽

pp. 369

Author(s):

A. Kashlinsky

Keyword(s):

Large Scale ◽

Small Scale ◽

Microwave Background ◽

Large Scale Data ◽

Scale Data ◽

Microwave Background Anisotropies

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Fault Diagnosis of Pump Based on a Hybrid HMM/SVM Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.188.629 ◽

2011 ◽

Vol 188 ◽

pp. 629-635

Author(s):

Xia Yue ◽

Chun Liang Zhang ◽

Jian Li ◽

H.Y. Zhu

Keyword(s):

Fault Diagnosis ◽

Hybrid Model ◽

Large Scale ◽

Recognition Accuracy ◽

Recognition Rate ◽

Double Layers ◽

Support Vector ◽

Practical Applications ◽

Average Recognition Rate ◽

Svm Model

A hybrid support vector machine (SVM) and hidden Markov model (HMM) model was introduced into the fault diagnosis of pump. This model had double layers: the first layer used HMM to classify preliminarily in order to get the coverage of possible faults; the second layer utilized this information to activate the corresponding SVMs for improving the recognition accuracy. The structure of this hybrid model was clear and feasible. Especially the model had the potential of large-scale multiclass application in fault diagnosis because of its good scalability. The recognition experiments of 26 statuses on the ZLH600-2 pump showed that the recognition capability of this model was sound in multiclass problems. The recognition rate of one bearing eccentricity increased from SVM’s 84.42% to 89.61% while the average recognition rate of hybrid model reached 95.05%. Although some goals while model constructed did not be fully realized, this model was still very good in practical applications.

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