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The purpose of this study is to achieve an accurate intelligent fault diagnosis of rolling bearing.

To extract deep features of the original vibration signal and improve the generalization ability and robustness of the fault diagnosis model, this paper proposes a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion. The original vibration signals are normalized and matrixed to form grayscale image samples. In addition, multiscale samples can be achieved by convoluting these samples with different convolution kernels. Subsequently, MCNN is constructed for fault diagnosis. The results of MCNN are put into a data fusion model to obtain comprehensive fault diagnosis results.

The bearing data sets with multiple multivariate time series are used to testify the effectiveness of the proposed method. The proposed model can achieve 99.8% accuracy of fault diagnosis. Based on MCNN and decision fusion, the accuracy can be improved by 0.7%–3.4% compared with other models.

The proposed model can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model. For a long time series of vibration signals with noise, the proposed model can still achieve accurate fault diagnosis.

The purpose of this study is to analyze the intelligent semisupervised fault diagnosis method of aeroengine.

A semisupervised fault diagnosis method based on denoising autoencoder (DAE) and deep belief network (DBN) is proposed for aeroengine. Multiple state parameters of aeroengine with long time series are processed to form high-dimensional fault samples and corresponding fault types are taken as sample labels. DAE is applied for unsupervised learning of fault samples, so as to achieve denoised dimension-reduction features. Subsequently, the extracted features and sample labels are put into DBN for supervised learning. Thus, the semisupervised fault diagnosis of aeroengine can be achieved by the combination of unsupervised learning and supervised learning.

The JT9D aeroengine data set and simulated aeroengine data set are applied to test the effectiveness of the proposed method. The result shows that the semisupervised fault diagnosis method of aeroengine based on DAE and DBN has great robustness and can maintain high accuracy of fault diagnosis under noise interference. Compared with other traditional models and separate deep learning model, the proposed method also has lower error and higher accuracy of fault diagnosis.

Multiple state parameters with long time series are processed to form high-dimensional fault samples. As a typical unsupervised learning, DAE is used to denoise the fault samples and extract dimension-reduction features for future deep learning. Based on supervised learning, DBN is applied to process the extracted features and fault diagnosis of aeroengine with multiple state parameters can be achieved through the pretraining and reverse fine-tuning of restricted Boltzmann machines.

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

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.

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.

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.

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

The purpose of this study is to investigate the understanding and application of crime of sabotaging production and operation in internet era, and, at the same time, discuss the basic position for criminal law interpretation in cyberspace.

Doctrinal analysis and case study.

Along with the advent of the internet era, how to apply the traditional crime of sabotaging production and operation in virtual space has attracted people’s attention. The controversy caused by the conviction of malicious application of fake transactions is a typical example. The legal interest protected here includes not only the property value of the means of production itself, but also the expectation interest that can be obtained by normal production and operation activities. There is no reliable basis to believe that overlap of articles between special provision and general laws occurs in crime of sabotaging production and operation and crime of intentional damage of property. The production and operation activities carried out online can also be covered by crime of sabotaging production and operation, without doubt. Ejusdem Generis Rule should be fully respected, but crime of sabotaging production and operation has a dual structure of means behavior and purpose behavior, where the purpose behavior, sabotaging production and operation, is the key to the conviction. However, it is not necessarily premised on physical damage and violent characteristics. The understanding and application of traditional crimes should keep pace with the times in the internet era, and we should not stick to a completely rigid subjective interpretation.

This study demonstrates the possible application of crime of sabotaging production and operation in cyberspace, and clarifies many misunderstandings about this crime.

This paper aims to provide an in-depth analysis of the process of standards setting based on the case of long-term evolution time division duplex (LTE TDD) deployment in China.

Using actor-network theory (ANT) as a theoretical framework, multi-level analyses are presented to explain the process of adoption of LTE TDD at a global level.

Findings identified the complex interaction between the social and technical aspects of fourth-generation (4G) by highlighting the co-evolving nature, diversity and interface that constitute the next-generation network environment.

ANT provides a framework of ideas for describing the process of technology adoption and for developing stories that explain it.

The findings shed light on a critical insight of the interrelationships between TD-SCDMA and LTE TDD and identify the policy successes and failures of 4G mobile networks.

The purpose of this paper is to analyze parameters that influence direct maintenance cost (DMC) in the civil aircraft operational phase. Reducing direct maintenance cost of civil aircrafts is one of the important ways to improve economy. DMC prediction can provide decision support for the optimization of the design parameters optimization to realize the objection in decreasing the maintenance cost, and it can also improve the aircraft competitiveness.

The paper analyzes some parameters comprehensively, which influence DMC in the civil aircraft’s operational phase. Based on the analysis of the influential parameters and the characteristics of data in the period of civil aircraft’s designing period, the paper presents prediction support method based on fuzzy support vector machine (FSVM) and realizes quantitative forecast of DMC in the aircraft design phase.

The paper presents the process of DMC analysis and model in the aircraft design phase, the DMC prediction model is used in newly developed aircrafts.

The numerical examples using B737NP fleet data in the paper have proved the effectiveness of the proposed method.

The paper establishes the prediction model of civil aircraft DMC based on FSVM. The model can handle fuzzy data and small sample data which contain noise. The results prove that the method can satisfy the demand of the real data in civil aircraft designing.

Based on the conservation of resources theory, the authors propose a research model depicting the positive relationship between team reflexivity and work-to-family enrichment via the mediation of thriving at work, with the moderation of transformational leadership. This paper aims to discuss the aforementioned idea.

The authors collected data from 367 employees in 79 teams at three time points. The authors test the model by using a multilevel moderated mediation analysis.

Results of this paper indicate that thriving at work partially mediates the relationship between team reflexivity and work-to-family enrichment. Furthermore, transformational leadership enhances the positive relationship between team reflexivity and thriving at work.

Organizations are advised to encourage employees' involvement in team reflexivity, facilitate their thriving at work and raise managers' awareness of work-family issues. Exemplary measures include nurturing open communication and providing training programs that encourage positivity in the workplace. By doing so, organization could strengthen the relationship between team reflexivity and work-to-family enrichment.

This research demonstrates the positive relationship between team reflexivity and work-to-family enrichment, deepening theoretical understanding of the antecedents of the construct. The findings of moderated mediation analysis shed light on the mechanism through which team reflexivity affects work-to-family enrichment, and the role that transformational leadership plays.

This paper aims to theoretically investigate a knowledge management model from the combined perspective of knowledge acquisition and knowledge application and its effect on organizational performance.

This study reviews prior research on knowledge acquisition and knowledge application, puts forward the concepts of “the extensiveness of knowledge acquisition” and “the concentration of knowledge application” and more importantly proposes an integrated model by combining these two dimensions. Four case examples of enterprises are subsequently described and analyzed to illustrate the sources of knowledge acquisition, the objects of knowledge application and their influences on organizational performance.

Four knowledge management modes and their impacts are confirmed in this study. Specifically, the organization of the turbojet engine mode (high extensiveness of knowledge acquisition and high concentration of knowledge application) can achieve good performance. The pipeline mode (high extensiveness of knowledge acquisition and low concentration of knowledge application) is the second, which has limited influence on good organizational performance. Organizations with the flashlight mode (low extensiveness of knowledge acquisition and high concentration of knowledge application) can achieve limited performance under the appropriate environment. The candle mode (low extensiveness of knowledge acquisition and low concentration of knowledge application) is the worst, performance of which is poor due to the break of the knowledge chain.

This paper holds that organizations should actively use the turbojet engine mode, adopt the pipeline mode and the flashlight mode cautiously, and avoid falling into the candle mode.

To the best of the authors’ knowledge, this study is among the first to propose the concepts of “the extensiveness of knowledge acquisition” and “the concentration of knowledge application,” and provides a combined model for analyzing differences in organizational performance from the perspective of knowledge.

This study aims to use the new product development (NPD) perspective to understand why traditional leading telecom equipment companies, such as Alcatel-Lucent (ALu), have stagnated but the newcomer Huawei has achieved steady growth.

This paper takes the form of a comparative case study.

Three significant differences in NPD process between the companies were discovered: first, although both companies claim that they are market-oriented, Huawei’s NPD projects are customer-driven, and ALu projects are joint considerations of customer demand and technology leadership; second, Huawei uses a design-to-value strategy, and ALu applies a design-for-quality-premium strategy; third, resources are allocated and shared at the corporate level in Huawei and at the business division level in ALu.

This study offers several implications for NPD managers. First, holding a market leader position is more important than being a technology leader. Companies must fundamentally change their mind-sets, restructure NPD models and prioritize and empower marketing and sales departments in the decision-making and management of NPD projects. Second, to maximize customer value, managers must balance cost and quality and avoid overengineering. A quality premium no longer necessarily leads to product competitiveness. Third, to improve the efficiency of NPD performance, companies must build up a mechanism to enable across-boundary resources.

This study highlights a number of key NPD strategy issues. It was conducted in the telecom equipment industry, but NPD managers of other industries will also gain useful insights from the discussion.

With the current rise of multinational enterprises (MNEs) from emerging economies (EE), more attention is now being directed to EE MNEs and what drives the internationalization of these companies. In this article we aim to provide more insights into the strategies and development of EE MNEs by conducting an in‐depth study of a Chinese high‐tech company in the communications equipment industry: Huawei. Our case study proposes that EE MNEs (1) tend to nurture their capability in the domestic market as a base before internationalization; (2) prefer to enter markets with fewer barriers in cultural, technological, economic, and institutional distances to accumulate experience and move up the value curve; and (3) use inward and outward linkages to complement their strengths and offset their weaknesses in the global market. Our study on the internationalization patterns of EE MNEs enriches and broadens current MNE theory.