Fan Manufacturers Research Articles

The Effects of Entrepreneurial Orientation on The Performance of Small and Medium Enterprises in Emerging Economies: Evidence from Pakistan’s Electric Fans Industry

Entrepreneurial orientation (EO) is a crucial multidimensional concept of entrepreneurship that encompasses the entrepreneur’s autonomy, attitude towards risk-taking, proactiveness, and competitiveness of entrepreneurs. EO influences the manner in which entrepreneurs perceive and react to their business environment. The majority of research on EO has been conducted in developed countries such as the USA, where institutional development is well established. There is a scarcity of studies focusing on emerging economies. In order to bridge this gap, the current study aims to investigate the construct and application of EO in Pakistan. The study focuses on Pakistan’s electric fan manufacturing industry to gain insight into the implementation of EO and its impact on the performance of small and medium-sized enterprises (SMEs) in emerging economies. In addition, the study aims to provide evidence-based knowledge regarding the causal relationship between EO and SME performance in Pakistan, as well as the impact of moderating variables, through hierarchical regression analysis and an analysis of a sample of 247 SMEs. The study provides solid evidence that Pakistan's electric fan manufacturing industry demonstrates all five dimensions of EO, as outlined in the existing literature. Furthermore, the study highlights the influence of age, education, and experience as moderating variables in the relationship between EO dimensions and the performance of SMEs in Pakistan.

Redefining fan manufacturing: Unveiling industry 5.0's human-centric evolution and digital twin revolution

Industry 5.0 has the capacity to surpass the technology -oriented efficiency of Industry 4.0 and advance sustainable development objectives such as prioritizing human needs, ensuring socio-environmental sustainability, and enhancing resilience. Digital twins and simulation technologies improve manufacturing, evaluate products and operations, and predict any potential adverse consequences. With digital twin technology, everything that exists in the physical world will eventually be duplicated in the digital realm. Within the context of Industry 5.0, this study aims to investigate the impact of digital twin technology on the fan manufacturing sector. The proposal for implementing the enabling industry 5.0 application was presented to the chief engineers of eight distinct fan manufacturers. Out of these, five responded positively and their feedback was subsequently followed up on. Three different avenues such as production, supply chain, and testing transparency were proposed for industry 5.0 implementation. The exploration of testing transparency is being undertaken based on a consensual decision. The Web of Things standard that enables digital twin generation in industry 4.0 is implemented to enable the testing transparency. This data was linked with the internal digital twin of fan motor created using ANSYS. This digital twin can predict the lifespan of the motor by analyzing the temperature of the motor housing surface. Toward sustainability and resilience with Industry 4.0 and Industry 5.0 may provide insights into this alignment.

Analysis of modern fan manufacturing variations and their links to Jet-engine performance

AbstractEvery manufacturing procedure is subject to tolerance variations. Over the years, a set of key characteristic features (KCF) that can explain the effect of manufacturing variations on the aero-mechanical performance of a fan blade has been devised and monitored to ensure conformality and good performance. The KCFs are derived from a cloud of coordinate measurement machine (CMM) points and are defined on approved engineering drawings for the manufactured part. In this paper, it is demonstrated that some of the traditional, common wisdom KCFs are not adequate to explain the engine performance deviation behaviour on a test bed at the sea-level condition. On the other hand, good correlation is found by analysing a set of engineering parameters drawn from a new inverse-mapping procedure of the CMM data. It is further demonstrated that a deviation measured via CMM or 3D structured light (GOM) data in cold conditions can be translated to a variation in the hot running shape of the blade. Having identified the key blade features, a cheap alternative to modifying the manufacturing procedure is devised to recover the fan performance by optimising its leading-edge shape.

Hybrid optimal deep learning with IoT based smart based monitoring and maintenance system for axial flow fan using feature optimization

Axial flow fan is a mechanical fan that generates airflow in the same direction as its rotational axis. These fans find widespread use in various applications, including ventilation, cooling, and air circulation across industrial, commercial, and residential settings. However, designing these fans can be challenging in the fan manufacturing industry due to the need to accommodate diverse operating conditions. This complexity arises from the fact that multiple design parameters significantly influence fan performance, requiring careful consideration and optimization to ensure efficient operation across various scenarios. In this paper, we present a technique for monitoring and maintenance of axial flow fans using hybrid optimal deep learning with IoT system. Our method leverages the pre-trained U-Net architecture to extract hidden features effectively from the dataset. Furthermore, we introduce an improved triple tree-seed optimization (IT2SO) algorithm for feature optimization, which identifies the most optimal features among the extracted ones. To make informed decisions about axial flow fan process monitoring, we propose the deep boosted hybrid learning (DBHL) technique as the decision model to maintain the proper operation. To validate the effectiveness of proposed IT2SO+DBHL technique, we have conducted experiments using the air movement and control association international (AMCA) dataset. The results demonstrate the superior performance of our monitoring approach compared to existing techniques across various evaluation measures.

Dangers of applying fan manufacturer data to equipment sound ratings

Equipment sound ratings are a valuable tool for consulting engineers; however, the basis of these ratings is not always clear. Custom AHU manufacturers most commonly utilize fan manufacturer data in accordance with AMCA 301 Methods for Calculating Fan Sound Ratings from Laboratory Test Data, and the AMCA 311 Certified Ratings Program. This paper will compare fan manufacturer data to test data collected in accordance with the mapped sound rating approach detailed in AHRI Standard 260, discuss possible sources of error in applying this data, and propose a best practice when comparing ratings from multiple custom AHU suppliers.

Combining Deep Neural Network with Genetic Algorithm for Axial Flow Fan Design and Development

Axial flow fans are commonly used for a system or machinery cooling process. It also used for ventilating warehouses, factories, and garages. In the fan manufacturing industry, the demand for varying fan operating points makes design parameters complicated because many design parameters affect the fan performance. This study combines the deep neural network (DNN) with a genetic algorithm (GA) for axial flow design and development. The characteristic fan curve (P-Q Curve) can be generated when the relevant fan parameters are imported into this system. The system parameters can be adjusted to achieve the required characteristic curve. After the wind tunnel test is performed for verification, the data are integrated and corrected to reduce manufacturing costs and design time. This study discusses a small axial flow fan NACA and analyzes fan features, such as the blade root chord length, blade tip chord length, pitch angle, twist angle, fan diameter, and blade number. Afterwards, the wind tunnel performance test was performed and the fan performance curve obtained. The feature and performance test data were discussed using deep learning. The Python programming language was used for programming and the data were trained repeatedly. The greater the number of parameter data, the more accurate the prediction. Whether the performance condition is met could be learnt from the training result. All parameters were calculated using a genetic algorithm. The optimized fan features and performance were screened out to implement the intelligent fan design. This method can solve many fan suppliers’ fan design problems.

Experimental Investigation on the Noise Characteristics of a Squirrel-Cage Fan with Different Blade Lengths

Squirrel-cage fans are widely applied in air conditioning systems, and their aerodynamic noise mainly related to blade length. The aerodynamic performance and noise spectrum of squirrel-cage fans are synchronously measured in an anechoic wind tunnel. The effect of blade lengths and different geometric configurations on the noise of a squirrel-cage fan is experimentally investigated. This paper focuses on the total sound pressure level and noise spectrum characteristics at different measurement points. Noise distributions of the outlet of the forward squirrel-cage fan exhibited axial-symmetry and large differences for those of vertical direction. In lower fan positions, the noise was greater than that of the top. In particular, it was found that blades were easy to generate higher noise when their length was reduced by 31.7%. The findings suggest that the broadband noise of the squirrel-cage fan should be fully considered for noise reduction. The purpose of this work is to provide a novelty reference for the low-cost modification method of cutting blades. The results show that fans with shorter blades have lower noise and kept an excellent performance. These finding have implications for fan manufacturers.

A Mechanistic Model for the Predictive Maintenance of Heavy-Duty Centrifugal Fans Operating With Dust-Laden Flows

Abstract Heavy-duty centrifugal fans are employed for the transport of dust-laden flows in industrial plants, for cement or steel production, or in energy production of plants and mine ventilation systems. These applications require the disposal of huge amounts of suspended particles, which can lead to a gradual erosion of the machine parts where impacts take place. The wear of the fan components can lead to premature failure of the machine, threatening human safety and reliability of the whole plant. The assessment of the wear severity of the machine, according to the process parameters, can aid the plant owner in scheduling overhaul operations along with the operative life of the machine. Moreover, through a reliable estimation of the wear severity, fan manufacturers can optimize the whole machine design process, from the material selection to the warranty time assessment. This work aims to develop a semi-empirical method capable of estimating the erosion severity of centrifugal fans employed for heavy-duty operations. A mechanistic model which accounts for the erosion on the blade leading edge is proposed. The model is derived by means of an analytical approach and accounts for a number of operating parameters (i.e.,, fan geometry, fan operating point, particle concentration in the flow, fluid properties, and material erosion resistance). A comparison of the theoretical model to the computational fluid dynamic (CFD) simulation results obtained through the use of multiphase particle tracking is also provided to assess the reliability of the present method.

Numerical and experimental study of a reversible axial flow fan

ABSTRACT Reversible axial flow fans, used in tunnel ventilation systems are designed in a way that as the direction of impeller rotation changes, suction and discharge directions change without any sensible change in flow rate and pressure. Also, their reversibility must be higher than 90%. Therefore, the blade profiles of these fans (S-shaped or elliptic) are designed symmetrically. In the current work, a reversible axial flow fan was studied experimentally and numerically in various ambient conditions and blade positions. Additionally, the experimental test of the fan was performed according to the AMCA-210 standard. Comparison of simulation and experimental results indicated the acceptable agreement of simulation results with experimental findings. The main achievement is that each blade can be studied before fabrication. Hence, the performance curves of designed fans can be predicted with reasonable accuracy without the substantial cost of fabrication and testing, which can be highly beneficial for fan manufacturers.

Manufacturing Defect Detection in Ceiling Fans using Image processing

During manufacturing process of ceiling fans there may be possibility that any step of manufacturing process can be skipped or improper completion due to malfunctioning of the system. In manufacturing process of fans, the outer plate, stator, rotor, axle and other parts are manufactured and assembled. If the winding machine is not working properly and improper windings is done and no-one can acknowledge that winding machine is not working properly then the whole batch should be designed with defect and this will surely create negative impact on the production process of the industry. So this proposed work will overcome this problem. This paper guides to detect whether the windings are proper or not and detection is carried out by taking picture of armature-windings. If there is any problem in the windings then our system will generate an alert so that other armatures can be protected from failures. Manual results are not so accurate all the time but in manufacturing process we need high accuracy. For this skilled labor is required and for skilled labor we have to pay more, this automated system reduce the need of skilled labor. If fan manufacturing industries use this image processing system then this will be very helpful in increasing production of fans within the completion time with more accuracy

Exploring the Relationship Between Innovation Capability, Total Quality Management and SMEs Performance in Electric Fan Industry of Pakistan: Moderating Effect of Relational Learning

Purpose – The main objective of this paper is to simultaneously determine the moderating role of relational learning in the relationship between Innovation Capability, Total Quality Management and SMEs Performance. This study attempts to expand the insights by determining these relationships in the context of a rapidly liberalizing and developing economy; Pakistan. By conducting this research with Pakistani data, the authors can assess whether the relationship between these three strategic resources shows systematic differences to those in developed economies.Design/methodology/approach – The authors of this study utilized the data collected through survey method in Electric Fan Industry of Pakistan. Well established scales were adopted from past research in order to measure the constructs used in the study. Structural equation modeling technique was used to test the hypotheses of the study. 239 fan manufacturing firms have been included in the sample. The authors used Smart PLS version 3 for data analysis.Findings – The findings revealed that relational learning moderates the relationship between predictor and criterion variables. Contrary to the previous findings, no significant relationship between Innovation Capability and SME Performance was found to exist; however, there existed a positively significant relationship between total quality management and SME performance.Research limitations/implications – Owing to the cross-sectional time frame that the study follows, it does not infer any causality. Having been limited to only fan manufacturing industry, the study calls for further research in different industry/country perspectives.Practical implications – The study highlights policy building prospects for Pakistani government based on the principles of innovation capability and quality enhancement through collaborating with business networks and exploring the potential of relational learning to help improve overall SMEs performance.Originality/value – This study contributes to the growing body of literature on SMEs-level innovation, TQM and relational learning in developing economies like Pakistan.

Organizational democracy and employee outcomes: The mediating role of organizational justice

AbstractIn the last two decades, scholars have proposed that implementing democratic practices and ideas in organizations results in significant improvements in employees' attitude and behavior. Using two widely advocated and debated dimensions of organizational democracy (empowerment and communication), the present study provides empirical evidence of this mentioned relationship between organizational democracy and employee outcomes (commitment, citizenship behavior, and turnover intentions) under mediating role of organizational justice. Based on the data of 262 employees working in fan manufacturing companies, structural equation modeling was employed to estimate the results. Results suggest that bringing democratization at workplace enhance employee commitment and citizenship behavior and reduce intentions to leave the organization. Furthermore, the study also supports organizational justice as a mediator, suggesting that the relationship between organizational democracy and employee outcomes becomes more strengthened once justice is introduced. The study provides empirical evidence to ideas and prepositions of many organizational democracy researchers working for decades.

Cooling air energy recovery in portable air compressor: an exergoeconomic analysis

This work aims to optimise the axial fan used in cooling system of a portable air compressor to obtain a lower power consumption of its drive motor that uses an internal combustion engine. The methodology starts with the thermodynamic modelling of compressor element and fan selection by comparison between fan manufacturer curves and experimental ones. At last, exergoeconomic analysis provides a cost estimation for products associate. Selecting a commercial fan-based more accurate according to cooling system data provided indicates that it is possible to reduce the ventilator power consumption, 21.5% for this case study. Based on the results of this study there is a reduction of 2.58% in fuel consumption, keeping the air flow in the cooling system indicating that the fan could be changed so that compressor to increase its energy efficiency and therefore operating costs of the equipment as it is directly reflected in spending on fuel.

National Fans Limited of Bangladesh

The case explores the marketing and business strategies of National Fan Limited (NFL). The company was one of the most successful domestic companies and leading ceiling fan manufacturers in Bangladesh. Specifically, the case focuses on the strengths and challenges of the company. The company had continuously expanded its operations and made a leading position in the industry since the early 1980s. It had adopted numerous dynamic strategies to sustain competitive advantage. The company, however, had experienced unprecedented challenges and setbacks to manage its growth. Besides numerous political challenges, the major issue was widespread existence of counterfeit products. Though the demand of ceilings fans had increased, the company had been struggling to keep pace with market growth due to the dearth of capacity. Other challenges were associated with raw materials and entrance of new rivals. The case allows students to explore strategies to overcome the challenges and increase market share.

Research on Energy Efficiency Status of Fans in Zhejiang Province

The fan industry has developed rapidly because of the large demand in the fields of thermal power, metallurgy and so on. There are many manufacturers in Zhejiang Province. However, energy efficiency projects are not carried out sufficiently in those factories. Compared with foreign standards on fan energy efficiency such as ANSI/AMCA 205 and ISO 12759, national standard GB 19761 still has some disparity in the scope of application, the energy efficiency classification and definition of energy efficiency. Using the experience of foreign standards and vigorously promoting the energy efficiency projects in the fan manufacturing enterprises could accelerate the development of domestic standard system on fan energy efficiency and entire fan industry.

Predicting the Performance of an Industrial Centrifugal Fan Incorporating Cambered Plate Impeller Blades

Application of computational methods to industrial fan design processes has progressed steadily over the past decade. The reducing cost of the computer hardware upon which codes run has brought the hardware within the reach of all industrial fan designers. However, the cost of commercially available codes remains high. Open source codes provide industrial fan designers with an alternative. The finite volume open-source solver OpenFOAM has been used by scholars to predict the performance of industrial centrifugal fans incorporating impeller blades constructed from cambered plate, but not by industrial fan designers. This paper presents a modelling approach which we developed for application as part of an industrial fan manufacturers order related design process. We compare numerical performance predictions with experimental results both at peak pressure and at peak efficiency conditions. As a further possible investigation, the simulated flow field is used to predict the patterns of erosion of the impeller.

Energy Efficiency of an Axial Fan for Various Casing Configurations

The objective of this paper is to compare the measured and predicted performances of a tubeaxial fan for several casing configurations that are commonly proposed by fan manufacturers to their clients. This work is motivated by the European Commission Regulation 327/2011, which will impose target energy efficiency for fans driven by electric motors beginning 1 January 2013. The prediction is made with the computational fluid dynamics (CFD) commercial code STAR-CCM+. The agreement between the experimental and numerical results on fan performance curves is very satisfactory, which confirms that CFD simulations may advantageously replace testing in parametric studies since they predict the quantitative differences of aerodynamic performance observed experimentally between the different casing configurations quite well. Numerical simulations may, therefore, help manufacturers to improve the geometry of their fans in order to fulfill the requirements of the regulation.

TQM practices in electric fan manufacturing industry of Pakistan

This study aims to descriptively analyse and rank the TQM practices, activities, critical success factors, barriers and business outcomes in electric fan manufacturing industry of Pakistan according to company size and on ISO-9000 certification basis. SMEs of electric fan manufacturing industry of two industrial cities of Gujrat and Gujranwala of Punjab province of Pakistan were selected as unit of analysis and both ISO and non-ISO certified firms were approached. An adopted/bilingual questionnaire was used for data collection using snow ball sampling technique, resulting in 89 completed questionnaires for data analysis. Data was analysed in terms of mean differences among firms both size wise and certification (ISO-9000) wise. The results demonstrate that large sized and ISO-9000 certified firms are more actively involved in TQM practices and activities. However, the small and medium sized firms do not differ significantly in different TQM practices and activities. Top management commitment is considered as the most important success factor by ISO-certified firms while top management training is considered as most important factor by non-certified firms. Lack of resources is the most crucial barrier for both certified and large firms. However, all type of firms consider profit making and decrease in unit cost as desired business outcomes in manufacturing industries. Large companies/ISO-9000 certified companies observed high business outcomes from their counterparts.

The Information Construction of Wind Farm Based on SIS System

In the wind farms, fans are widely distribution with large amount and they are away from the monitoring center, working environment is poor. In order to ensure the safe and stable operation of the wind farms, the wind power operation requirements need to be satisfied, own better function performance and stability of remote monitoring system to improve the management efficiency. In view of this, the power group increasing highly requirements on wind farm group management,but at present, the single SCADA system which the fan manufacturers offered has failed to meet the requirements. On the basis, this article designs the wind farm supervisory information system (SIS), and realizes wind farm cluster control, data analysis, performance optimization and fault warning.

Toward the prediction of fan noise: From low-speed to high-speed turbomachineries.

Environmental concern and comfort trigger more and more acoustic specifications and regulations on aircraft impact on airports and on ventilation systems in buildings or transportation systems (cars, trains, or airplanes), which in turn impose lower and lower maximum noise levels to such systems. For instance, turbofan engines have increased their bypass ratio in order to improve the aircraft performance while diminishing the nominal speed of rotation. The jet noise is then reduced, and the fan noise becomes a dominant source of noise, especially at approach. A quick calculation of the overall noise generated by a given fan geometry, either low speed or high speed, would be a valuable asset to any fan design designer and manufacturer prior to any installation in a building or an airplane, for instance. However, an accurate prediction of the sound by any full turbomachinery still remains a challenging goal and a daunting task to be achieved by a direct computation. In the present study, the noise predictions will then rely on a strip theory combined with an acoustic analogy based on the wall pressure fluctuations. For the low-speed fans, the model is an extension of the development by Schlinker & Amiet for helicopters. Turbofan engines induce two additional difficulties to noise modeling: the cascade effects and the duct configuration which are presently modeled.