A New Fault Diagnosis of Multifunctional Spoiler System Using Integrated Artificial Neural Network and Discrete Wavelet Transform Methods

Kordestani, Mojtaba; Samadi, M. Foad; Saif, Mehrdad; Khorasani, K.

doi:10.1109/jsen.2018.2829345

Cited by 71 publications

(22 citation statements)

References 46 publications

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“…Palmer suggested that data/information collection and processing as well as database technology played an important role in aircraft fault diagnosis [31]. Kordestani proposed an integrated system to classify fault localization [32]. Likewise, a multi-fault diagnosis expert system was proposed by Anami, using a dynamic fault tree method with continuous optimization [33].…”

Section: Fault Diagnosis and Reliability Evaluation Of Complex Systemmentioning

confidence: 99%

“…4. It is difficult for the existing reliability analysis model of the system to describe system uncertainty as well as for some unconventional influence factors (such as environment and human factors) to be described in the model [20,21,[24][25][26][27][28]30,32,33]. In a word, there are many methods for fault diagnosis and prediction of complex systems, and each method is not perfect.…”

Section: Svm Theorymentioning

confidence: 99%

“…As the system states increase, there would appear an exponential explosion of state space for the reliability analysis model, resulting in great difficulty in model construction and solution [21,23,24,26,27,29,30,32,33].…”

mentioning

confidence: 99%

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Industrial Big Data and Computational Sustainability: Multi-Method Comparison Driven by High-Dimensional Data for Improving Reliability and Sustainability of Complex Systems

Liu

Jia

2019

Sustainability

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Sustainable development is of great significance. The emerging research on data-driven computational sustainability has become an effective way to solve this problem. This paper presents a fault diagnosis and prediction framework for complex systems based on multi-dimensional data and multi-method comparison, aimed at improving the reliability and sustainability of the system by selecting methods with relatively superior performance. This study took the avionics system in the industrial field as an example. Based on the literature research on typical fault modes and fault diagnosis requirements of avionics systems, three popular high-dimensional data-driven fault diagnosis methods—support vector machine, convolutional neural network, and long- and short-term memory neural network—were comprehensively analyzed and compared. Finally, the actual bearing failure data were used for programming in order to verify and compare various methods and the process of selecting the superior method driven by high-dimensional data was fully demonstrated. We attempt to provide a sustainable development idea that continuously explores multi-method integration and comparison, aimed at improving the calculation efficiency and accuracy of reliability assessments, optimizing system performance, and ultimately achieving the goal of long-term improvement of system reliability and sustainability.

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Section: Fault Diagnosis and Reliability Evaluation Of Complex Systemmentioning

confidence: 99%

Section: Svm Theorymentioning

confidence: 99%

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Industrial Big Data and Computational Sustainability: Multi-Method Comparison Driven by High-Dimensional Data for Improving Reliability and Sustainability of Complex Systems

Liu

Jia

2019

Sustainability

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“…Focusing on the problem of monitoring the quality of the small scale resistance spot welding of titanium alloy, Wan et al [17] proposed different neural network models for weld quality prediction. Kordestani et al [18] introduced a new fault diagnosis method for the multifunctional spoiler (MFS) by using artificial neural networks and discrete wavelet transform. In addition, some hybrid methods were developed.…”

Section: Problem Statementsmentioning

confidence: 99%

A Novel Quality Defects Diagnosis Method for the Manufacturing Process of Large Equipment Based on Product Gene Theory

Chen

Guo

et al. 2019

Symmetry

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Focusing on the problems of quality information management and quality defects diagnosis in the manufacturing process of large equipment, a novel quality defects diagnosis method based on product gene theory and knowledge base was developed. First, a product gene model and a sectional encoding method for the quality control of the manufacturing process of large equipment were proposed. In that model, the processing surface was the minimum information granularity to meet the production characteristics of large equipment and to improve the flexibility of the product gene model. Then, a similarity evaluation rule and an optimization method of the weights of elements based on particle swarm optimization (PSO) were addressed to filter the available knowledge of product gene from the product gene knowledge base. Aiming at the characteristic of many-to-many between quality defects and quality influence factors in some cases, a fuzzy comprehensive evaluation (FCE) method was developed for the further localization of diagnosis knowledge. Finally, an experiment of bearing spacer was applied to illustrate the proposed quality diagnosis approach. In the experiment, the data from the target gene and knowledge genes were described reasonably. On this basis, available knowledge genes could be accurately filtered with the proposed similarity rule and the method of filtration, where the PSO was proved to be effective. The diagnosis results of the experiment show that multiple factors lead to the defects that were verified. Therefore, the proposed quality defects diagnosis method is an effective way for quality control.

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“…Aiming at the typical faults of bearing, some researches proposed the method based on WT and utilized it to diagnose the faults successfully [18,19]. Moreover, the method based on WT was used by Kordestani et al to diagnose the faults of spoiler system effectively [20]. Pointing at the faults of planetary gearboxes, Zhao et al also proposed a method based on WT, and proved the faults could be diagnosed effectively [21].Although WT has widely used in some fields, it has some imperfect aspects.…”

mentioning

confidence: 99%

Feature Extraction Method for Hydraulic Pump Fault Signal Based on Improved Empirical Wavelet Transform

et al. 2019

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There are many interference components in Fourier amplitude spectrum of a contaminated fault signal, and thus the segment obtained based on the spectrum can lead to serious over-decomposition of empirical wavelet transform (EWT). Aiming to resolve the above problems, a novel method named improved empirical wavelet transform (IEWT) is proposed. Because the power spectrum is less sensitive to the contaminated interference and manifests the presence of fault feature information, IEWT replaces the Fourier amplitude spectrum of EWT with power spectrum in segment acquirement, and threshold processing is also introduced to eliminate the bad influence on the acquirement, and thus the best decomposition result of IEWT can be obtained based on feature energy ratio (FER). The loose slipper fault signal of hydraulic pump is tested and verified. The result demonstrates that the proposed method is superior and can extract the fault feature information accurately.

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A New Fault Diagnosis of Multifunctional Spoiler System Using Integrated Artificial Neural Network and Discrete Wavelet Transform Methods

Cited by 71 publications

References 46 publications

Industrial Big Data and Computational Sustainability: Multi-Method Comparison Driven by High-Dimensional Data for Improving Reliability and Sustainability of Complex Systems

Industrial Big Data and Computational Sustainability: Multi-Method Comparison Driven by High-Dimensional Data for Improving Reliability and Sustainability of Complex Systems

A Novel Quality Defects Diagnosis Method for the Manufacturing Process of Large Equipment Based on Product Gene Theory

Feature Extraction Method for Hydraulic Pump Fault Signal Based on Improved Empirical Wavelet Transform

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