No abstract

When the vibration signals of the rolling bearings contain strong interference noise, the spectrum division of the vibration signals is seriously disturbed by the noise. e traditional empirical wavelet transform (EWT) decomposes signals into a large number of components, and it is difficult to select suitable components that contain fault information. In order to address the problems above, in this paper, we proposed the improved empirical wavelet transform (IEWT) method. e simulation experiment proved that IEWT can solve the problem of a large number of EWT components and separate the impact component effectively which contains bearing fault information from noise. e IEWT method is combined with the support vector machine (SVM) to diagnosis the fault of the rolling bearings. e permutation entropy (PE) is used to construct feature vectors for its strong induction ability of dynamic changes of nonstationary and nonlinear signals. e crucial parameter penalty factor C and kernel parameter g of SVM are optimized by quantum genetic algorithm (QGA). Compared with traditional EWT and variational mode decomposition (VMD) methods, the effectiveness and advantages of this method are demonstrated in this study. e classification prediction ability of SVM is also better than that of K-nearest neighbor (KNN) and extreme learning machine (ELM).

The primary aim of this paper is to investigate whether equal opportunity and diversity pronouncements, both internally through organizations' own administrative policies or externally through imposed governmental legislations, benefit those who are the main subject of such initiatives (i.e., employees). While a majority of current research on equality and diversity has been dominated by writings on developed and specifically Western nations, this paper tackles such one-sidedness in previous research and takes the current understanding further by providing employee perspectives on equality and diversity in employment to encompass less developed nations with a particular focus on Iran. Using a qualitative research approach data were collected from employees across two construction and manufacturing industries. Based on the analysis of the data, we found, first, shared religious beliefs and language to be envisaged as playing a crucial part in establishing the ethnic minority workers' affiliation to a workgroup; second, the prospects for implementing declared equality and diversity polices to fade away as the employee

Yingying (2015) The applicability of Grant's framework in the dynamic digital age: a review and agenda for future research. European Business Review, 27 (6). pp. 656-678.

No abstract

PurposeThe purpose of this paper is to assess the current state of employee training practices in a sample of Iranian‐based organisations.Design/methodology/approachThe research approach adopted for the study conforms to qualitative research in the form of multiple case study design. Semi‐structured interview is adopted to collect qualitative data. To enhance the validity of the information derived from the interviews, the interview data are supplemented by some observations and examination of the related documents.FindingsData from managers and employees from different functional areas support a capital approach to employee training. The results further indicate that the current approach to training has an upward impact on employee turnover. Overall, the data suggest that the effect of training on employee motivation and productivity is indirect through management's approach and orientation towards training. This, in turn, causes a gap between employee's expectation and perception toward the effectiveness of organisational training.Research limitations/implicationsThere is no sufficient evidence to correlate directly the contribution of training programmes to the overall performance of both employee and organisation. The need for more empirical data should focus on more rigorous testing of the implications of the current state of training interventions for other human resource management practices.Practical implicationsThere is a fundamental need for the Iranian managers, first, to reconsider their understanding of and rationale for training interventions; second, to realise the increased importance of employee training in achieving sustainable competitive advantage in the long term; third, to encourage employees to recognise their training needs; and last and fourth, to recognise the importance of employee training in enhancing organisational commitment. In respect of the management of international companies wishing to exploit business opportunities in Iran, there is a need on their part to know the Iranian culture, its difference from other Middle Eastern countries, and therefore to overcome such existing cross‐cultural challenges.Originality/valueThe degree to which the adoption of training strategies reflects new and different attitudes and practices among the non‐managerial employees is seen to be a major shortcoming of previous research. This study addresses such limitation by collecting data from multiple perspectives in the novel context of the Middle East with a particular focus on Iran.

A quantizing method, single parameter adjustment method (SPAM), is proposed so that the selection of controlled damping parameters of the magnetorheological (MR) damper models can be well founded. By using SPAM, only one parameter is identified each time, and the controlled damping parameters are selected according to their damping controllability. The relationships between the selected parameters and applied currents are determined by curve fitting. Genetic algorithm (GA) and pattern search (PS) are used to identify parameter values of the MR damper models. A modified Bouc–Wen model is considered and its parameters are obtained using these methods. Then the experimental data with different frequencies, amplitudes, and currents are used to verify the proposed SPAM. The results show that the simulation data agree well with the measured experimental data. Compared with the traditional identification method that relies on assumption and visual inspection, errors produced by SPAM are greatly reduced. At last, Bouc–Wen model and modified Dahl model are considered and analyzed using SPAM.

Variational mode decomposition (VMD) has been applied in the field of rolling bearing fault diagnosis because of its good ability of frequency segmentation. Mode number K and quadratic penalty term α have a significant influence on the decomposition result of VMD. At present, the commonly used method is to determine these two parameters adaptively through intelligent optimization algorithm, namely, the parameter-adaptive VMD (PAVMD) method. The key of the PAVMD method is the setting of an objective function, and the traditional PAVMD method is prone to overdecomposition or underdecomposition. To solve these problems, an improved parameter-adaptive VMD (IPAVMD) method is proposed. A new objective function, the maximum average envelope kurtosis (MAEK), is proposed in this paper. The new objective function fully considers the equivalent filtering characteristics of VMD, and squared envelope kurtosis has good antinoise performance. In the optimization method, this paper uses an improved particle swarm optimization (PSO) algorithm. The MAEK and PSO can make sure the IPAVMD method reaches the best complete decomposition of the signal without an underdecomposition or overdecomposition problem. Through the analysis of simulation data and experimental data, the performance of the IPAVMD and the traditional PAVMD is compared. The comparison results show that the proposed IPAVMD has better performance and stronger robustness than the traditional method and is suitable for both single-fault and multiple-fault cases of rolling bearings. The research results have certain theoretical significance and application value for improving the fault diagnosis effect of rolling bearings.