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Article
Publication date: 20 April 2020

Changchang Che, Huawei Wang, Xiaomei Ni and Qiang Fu

The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing.

413

Abstract

Purpose

The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing.

Design/methodology/approach

The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN.

Findings

The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models.

Originality/value

The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

Details

Industrial Lubrication and Tribology, vol. 72 no. 7
Type: Research Article
ISSN: 0036-8792

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Article
Publication date: 9 August 2023

Siyu Su, Youchao Sun, Chong Peng and Yuanyuan Guo

The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.

181

Abstract

Purpose

The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.

Design/methodology/approach

This paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.

Findings

The global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.

Originality/value

Unlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.

Details

Engineering Computations, vol. 40 no. 7/8
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 18 April 2024

Zhanghuang Xie, Xiaomei Li, Dian Huang, Andrea Appolloni and Kan Fang

We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution…

346

Abstract

Purpose

We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution approaches to solve such problem.

Design/methodology/approach

We propose a mathematical formulation for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines, and develop a simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length to solve the problem.

Findings

The simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length (SAHH-osla) that we proposed can be quite efficient in solving the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

Originality/value

To the best of our knowledge, we are one of the first to consider both cost-related and time-related criteria for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

Details

Industrial Management & Data Systems, vol. 124 no. 11
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 17 January 2020

Chen Ji, Qin Chen and Ni Zhuo

The purpose of this paper is to explore how consumers’ trust is enhanced by e-commerce-based agribusiness companies. It also aims to shed light on the role of social commerce in…

1272

Abstract

Purpose

The purpose of this paper is to explore how consumers’ trust is enhanced by e-commerce-based agribusiness companies. It also aims to shed light on the role of social commerce in improving consumers’ trust.

Design/methodology/approach

To achieve the research purpose, an in-depth multiple case study is performed. In this study, three cases in short food supply chain (SFSC) in China are selected, and they are all e-commerce agribusiness companies. They adopted common ways to build up, maintain and reinforce consumers’ trust.

Findings

It is revealed that the companies innovatively adopted social commerce, both online and offline, to overcome the trust problems usually faced by e-commerce companies. It is also shown that offline contact with potential consumers is an important first step for agribusiness e-commerce entrepreneurs to build up trust with consumers.

Research limitations/implications

By adopting a multiple case study method, the research has limited generalizability to other types of SFSCs. Since the findings are from Chinese agribusiness e-commerce companies, the generalization to other sectors must be done with caution.

Practical implications

Some managerial implications are given as follows: first, offline contact with consumers could be realized through different channels. Taking advantage of existing social network or trying to find consumers in urban communities might be effective ways. Second, trust building with consumers is not an easy task, managers need to emphasize trust building, trust maintaining, as well as trust reinforcing with consumers. In agri-food sector, managers might need to specifically address the importance of food safety and quality so as to not lose consumer trust in one night.

Originality/value

The study has mainly two contributions: first, it has managerial implications for agribusiness e-commerce entrepreneurs, addressing the important role of social presence in building up consumer trust. Second, it contributes to social presence and social relations literature by providing new empirical evidence from e-commerce in agri-food sector and in developing countries.

Details

Journal of Agribusiness in Developing and Emerging Economies, vol. 10 no. 1
Type: Research Article
ISSN: 2044-0839

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Article
Publication date: 20 September 2024

Jiaping Zhang and Xiaomei Gong

The research attempts to estimate how the use of WeChat, the most popular mobile social networking application in contemporary China, affects rural household income.

36

Abstract

Purpose

The research attempts to estimate how the use of WeChat, the most popular mobile social networking application in contemporary China, affects rural household income.

Design/methodology/approach

Our materials are 4,552 rural samples from the Chinese General Social Survey, and a treatment effect (TE) model is employed to address the endogeneity of WeChat usage.

Findings

The results prove that WeChat usage has a statistically significant and positive correlation with rural household income. This conclusion remains robust after using alternative variables to replace the explanatory and dependent variables. Our research provides two channels through which WeChat usage boosts rural household income, namely, it can promote their off-farm employment and participation in investment activities.

Originality/value

Theoretically, the study provides several micro-evidences for understanding the impact of mobile social networks on rural household welfare. Further, our findings may shed light on the importance of digital technology applications in rural poverty alleviation for developing countries.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

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