Intelligent Machinery Research Articles

Two-dimensional vector bending sensor based on single excessively tilted fiber grating.

We introduced a two-dimensional vector-bending sensor using excessively tilted fiber grating (ExTFG) encapsulated in a non-neutral axis position within a flexible cylinder. The asymmetrical structure enabled both TM and TE modes of the ExTFG to achieve the competency of one-dimensional vector bending sensing by cleverly leveraging the orthogonal relationship between these modes to achieve the effect of orthogonal cascading of two one-dimensional sensors. The resonance wavelength changes of the TM and TE modes were monitored under equal excitation states to achieve two-dimensional vector bending sensing, which significantly simplified the manufacturing complexity of the sensor; the longer the off-axis distance, the higher the sensitivity, and the smaller the maximum measurable curvature. Through an optimal parameter designation for the sensor, the results showed that the transmission spectra of TM and TE modes varied significantly depending on the curvature and direction of the sensor bending with a maximum sensitivity of -105 pm/m-1 and 105.5 pm/m-1, -70.25 pm/m-1 and 67.5 pm/m-1, respectively. Subsequently, the direction and curvature of the vector bending could be reconstructed using the established algorithm with the measured wavelength shifts of TM and TE modes. The average relative error was about 2.02% for the reconstructed direction angle and about 3.61% for the reconstructed curvature. The proposed sensor offered higher sensitivity than those based on FBGs and improved stability compared to those based on optical fiber interferometers. Therefore, it holds promising applications in structural health monitoring in complex environments and intelligent machinery.

Design and implementation of a multifunctional desktop robot for computer peripherals

Abstract As an important component of the intelligent machinery field, desktop robots not only enrich people’s entertainment lives but are also gradually expanding into applications in education, healthcare, and other fields. However, existing products often struggle to meet the increasingly diverse needs due to short battery life and limited computing resources. In response to this challenge, this study designs a multifunctional desktop robot that can be used as a computer peripheral, which significantly improves power supply and computing capabilities. The design uses the STM32F4 series microcontroller as the main control chip, combined with high-performance servos and their self-developed driver boards, to achieve precise motion control. By integrating USB-HS PHY and USB-CDC protocol, the communication efficiency with the computer is greatly improved, and new screen-driving technology is developed using open-source routines, thus optimizing the user interaction experience. In addition, an innovative adaptive algorithm is incorporated into the firmware program, enhancing the robot’s working stability in different environments. Through systematic hardware debugging and software testing, the robot can achieve efficient performance and stable response when executing complex tasks. This research will promote the expansion of desktop robot technology into broader application fields and support future intelligent work and lifestyle.

An Ultrasonic Ridge-Tracking Method Based on Limiter Sliding Window Filter and Fuzzy Pure Pursuit Control for Ridge Transplanter

There are various types of fruits and vegetables that need to be planted on ridges. In order to allow for seedlings with a certain row space and seedling space, the ridge transplanter should be able to track along the ridge. Therefore, an ultrasonic ridge-tracking method and system were developed to let the ridge transplanter track the ridge accurately. The ultrasonic ridge-tracking method mainly contains a limiter sliding window filtering algorithm and a fuzzy look-ahead distance decision model. The limiter sliding window filtering algorithm was proposed to filter the abnormal measuring results to avoid disoperation of the steering mechanism. Moreover, the fuzzy look-ahead distance decision model was proposed to determine the optimal look-ahead distance in order to obtain a desirable tracking performance. Additionally, a comparison experiment of the proposed ultrasonic ridge-tracking method and the universal pure pursuit method was conducted. The experimental results show that the greatest mean absolute errors of the lateral deviations of the ultrasonic ridge-tracking method and universal pure pursuit were 10.56 mm and 13.11 mm. The greatest maximum absolute errors of the lateral deviations of the ultrasonic ridge-tracking method and universal pure pursuit were 18.87 mm and 23.23 mm. In addition, the greatest root mean square error of the lateral deviation of the ultrasonic ridge-tracking method and the universal pure pursuit method were 13.52 mm and 15.66 mm. According to the ridge-tracking performance of the proposed ultrasonic ridge-tracking method, it can be used in practical transplanting conditions. Moreover, in other fields, robots or intelligent machinery can also apply the proposed ultrasonic ridge-tracking method to track objects similar to ridges.

A hybrid network TEdgeNeXt for data-limited and resource-constrained fault diagnosis

In the field of intelligent machinery fault diagnosis, overcoming challenges arising from scarce labeled data and the demand for deployment on resource-constrained edge devices is imperative. To address these hurdles, this work aims to improve the ability of deep learning models to learn strong feature representations from limited data, while also reducing the model complexity. We presenting a novel network named TEdgeNeXt, the approach begins with a new signal-to-image conversion method, which is proved to be able to acquire less training data quantity. Structurally, the Convolutional (Conv.) Encoder initially is employed with depth-wise separable convolution to control the size of model rather than the traditional convolution, and the Split Depth-wise Transpose Attention (SDTA) encoder is consequently utilized by leveraging a multidimensional processing approach and the Multi-head Self-Attention which is across the channel dimensions instead of the spatial channel. By doing so, it effectively handles challenges such as high multiply-additions (MAdds) and increased latency through Flops and params. On the other hand, the fine-tune-based transfer learning technique is able to be extended in our approach for improving the capacity of generalizing. Ultimately, it indicates the noticeable improvements in Top-1 Accuracy (T1A), Mean Precision (MP), Mean Recall (MR), and Mean F1 score (MF1) across three distinct datasets.

Research on Bank Hall Service Management Strategy of Rural Commercial Bank

With the transformation and upgrading of financial technology, people's demand for personalized banking services is getting higher and higher. In order to meet people's growing financial needs, commercial banks have carried out a series of branch transformation and upgrading. As the main business place of physical network, the hall of rural commercial bank plays an irreplaceable role in electronic channels and virtual places. After decades of development, rural commercial banks have carried out a series of reforms and innovations. At present, the external layout of hall outlets, such as intelligent machinery, electronic equipment, and standardized network construction, has achieved initial results. Under the background of network transformation, the network construction of rural commercial banks has undergone great changes, but the concept of hall operation service has not matched the network construction in time, and can not adapt to the requirements of banking development under the new situation. In view of this situation, this paper explores the hall management and operation services of rural commercial banks, with the purpose of improving the hall service efficiency of rural commercial banks, improving the enthusiasm of employees, improving the service quality and efficiency, and increasing the profitability of banks.Taking the branch hall management of commercial banks in rural areas as the entry point, this paper uses literature research, process reengineering, salary management and other relevant theoretical research methods, SWOT analysis and other methods to analyze the problems existing in the transformation and upgrading process of branch halls of rural commercial banks, such as unreasonable staffing, cumbersome processes, low efficiency and low customer satisfaction. This paper explores the causes of such problems according to the problems, that is, backward management system, market positioning, low entry conditions for competitors, and low scale effect. Finally, it aims to improve system management, optimize organizational framework, integrate hall zoning, refine the division of labor, increase scientific and technological support, and innovate service modes. Improve the assessment mechanism, and put forward constructive suggestions on hall management and marketing strategy in the process of network transformation and upgrading to employees. The research in this paper can improve the service quality and efficiency of hall staff, improve the post adaptability of hall staff, thus improve marketing performance, realize the transformation and upgrading of outlets, and achieve greater economic benefits of rural commercial banks.

Application of Closed-loop Theory in Deep Learning Training Guided by High-strength Intelligent Machinery

Artificial intelligence (AI) algorithms and continuous monitoring technologies have the potential to transform the way chronic illnesses are managed. We will also talk about the problems and potential that AI technology presents for CGM in individualised and preventive medicine. Furthermore, we assessed the AHCL system's usefulness in patients with impaired awareness of hypoglycemia (IAH) and those who correctly recognised hypoglycemia symptoms. The participants' ages varied from 37 to 15, and they had received diabetes medication for an average of 20 to 10 years. IAH was seen in 12 individuals (27%) with a Clarke's score of less than 3. Patients with IAH were older than those who did not have IAH. The baseline CGM readings and A1c were the same, but the estimated glomerular filtration rate (eGFR) was lower. Despite prior insulin treatment, the AHCL system resulted in an overall drop in A1c (from 6.9 0.5% to 6.7 0.6%, P 0.001). Only three patients (7%) received Clarke's three scores after six months on the AHCL system, resulting in a 20% absolute risk decrease for IAH (95% confidence interval: 7-32).

Analytical assessment of the machinery use efficiency in the technology of crop products production

The crop growing industry focuses, first of all, on highly productive and efficient machinery that meets all modern requirements of agricultural production. This machinery can significantly increase the productivity of agricultural labor and create comfortable working conditions. Digital transformation and digitalization of crop production requires introducing highly intelligent machinery and technologies for crop cultivation into the agro-industrial complex, which, in turn, necessitates requirements for the growth of intellectual abilities of agricultural workers. The current prices for modern machinery and technologies are very high; for many enterprises it is not possible to purchase them, therefore the relevance of state support from federal sources is very necessary. There is a negative trend in the numerical reduction of machinery in the region under study, as a result of which there is an increase in the specific load per unit of machinery, and that leads to accelerated wear. As a result, production volumes in crop growing are being reduced. Calculations show that the degree of exploitation of the combine harvester fleet in the region is slightly higher than that of the tractor fleet, so these trends should be taken into account in the process of planning the volume of investments for their acquisition.

Dual Structural Consistent Partial Domain Adaptation Network for Intelligent Machinery Fault Diagnosis

Dual Structural Consistent Partial Domain Adaptation Network for Intelligent Machinery Fault Diagnosis

Intelligent Machinery Fault Diagnosis With Event-Based Camera

Event-based cameras are the emerging bio-inspired technology in vision sensing. Different from the traditional standard cameras, the event-based cameras asynchronously record the brightness change per pixel, and have the great merits of high temporal resolution, low energy consumption, high dynamic range, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . While the event-based cameras have been initially exploited in several common vision-based tasks in the recent years, the investigation on machine condition monitoring problem is quite limited. This paper offers the first attempt in the current literature on exploring the contactless event vision data for machine fault diagnosis. A vibration event representation is proposed to transform the event records into typical data samples, and a deep convolutional neural network model is used for processing the event information. To enhance the model robustness against environmental noisy vision events, an event data augmentation method is proposed to introduce variations of the event patterns. A deep representation clustering method is further proposed to improve the pattern recognition performance with respect to different machine health conditions. Experiments on the event vision-based rotating machine fault diagnosis problem are carried out. It is extensively validated that high fault diagnosis accuracies can be obtained using the vision data from the event-based cameras, which are competitive with the popular accelerometer data. Considering the properties of flexibility, portability and data recognizability, the event-based cameras thus provide a promising new tool for contactless machine health condition monitoring and fault diagnosis.

An energy‐efficient model for PPE waste management in a closed‐loop supply chain

AbstractThe COVID‐19 crisis has significantly increased non‐decomposable personal protective equipment (PPE), such as gloves, masks, and shields. The improper disposal of these items poses a serious environmental threat. The present study explores the potential of recycling PPE to not only enhance the profitability of the supply chain but also address the environmental challenges associated with disposal. Specifically, it focuses on implementing a closed‐loop approach involving the manufacturer, the customer, and the collector. An extensive literature review on inventory models was conducted, resulting in a comprehensive data set. The numerical analysis, conducted using the Generalized Reduced Gradient Solver, validates the findings. The proposed model suggests that investing in a closed‐loop supply chain, employing an energy‐efficient approach, can effectively reduce carbon emissions at every stage of the system. This can be achieved by leveraging intelligent machinery and skilled labor to meet the growing demand for PPE while minimizing energy consumption. Furthermore, this research emphasizes the importance of implementing the reduce, reuse, and recycle (3Rs) phenomenon to manage waste effectively. This study provides valuable insights for businesses aiming to optimize waste management practices and contribute to economic‐environmental‐social sustainability.

Blockchain adoption in sustainable supply chains for Industry 5.0: A multistakeholder perspective

Industry 5.0 has introduced a novel interpretation of sustainable supply chains (SSCs) that emphasizes the importance of building a transparent and trustworthy network that can be continuously monitored and controlled through stakeholder collaboration as well as the use of advanced, intelligent machinery. The ultimate goal of SSCs is to meet specific economic, social, and environmental standards. The implementation of blockchain technology can significantly improve the reliability, efficiency, and security of the information exchanged among stakeholders in SSCs. However, these stakeholders inevitably possess varying informational advantages and exhibit divergent perspectives regarding the adoption of blockchain technology. This paper thus aims to examine the impediments to the adoption of blockchain technology in the context of SSCs with the goal of promoting blockchain adoption. To achieve this objective, this study analyzes the barriers to blockchain adoption from the perspectives of various stakeholders in SSCs and constructs a barrier severity assessment model that utilizes group decision-making methods to integrate all stakeholders’ attitudes. This study employs the PEEST (political, economic, environmental, social, and technological) framework to identify 27 barriers to the adoption of blockchain technology. Subsequently, an expertise-based group decision-making approach is used to quantify the prominence of various barriers according to various types of stakeholders. The results indicate that the five most intense barriers are storage constraints, insufficient economic incentives, high integration costs, a lack of functional appeal, and ambiguity regarding data disclosure and public data management regulations. This research makes novel theoretical and practical contributions, as it takes an empirical and all-encompassing approach to identifying obstacles to the adoption of blockchain technology and provides valuable insights for policymakers and practitioners to reference in overcoming these obstacles.

Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure Identification

Here, a novel hybrid method of intelligent fault identification within complex mechanical systems was proposed using parallel-factor (PARAFAC) theory and adaptive particle swarm optimization (APSO) for a support vector machine (SVM). The parallel-factor multi-scale analysis theory was studied to reconstruct tensor feature information based on a three-dimensional matrix for time, frequency, and spatial vectors. A multi-scale wavelet analysis was used to transform the original multi-channel experimental data acquired from a gearbox into a three-dimensional feature matrix of the multi-level structure. The optimal correspondence among the two-dimensional feature signals in the frequency and time domains for the different fault modes was established by the PARAFAC theory. An intelligent APSO algorithm was developed to obtain the optimal parameter structures of an SVM classifier. A comparison with the existing time–frequency analysis method showed that the proposed hybrid PARAFAC-PSO-SVM diagnosis model effectively eliminated the redundant information in the multi-dimensional tensor features but retained the important components. The PARAFAC-APSO-SVM hybrid diagnostic model achieved fast, accurate, and simple fault-classification and identification results, and could provide theoretical support for the application of the PARAFAC theory to complex mechanical fault diagnosis.

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.

The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review

Throughout human history, agriculture has undergone a series of progressive transformations based on ever-evolving technologies in an effort to increase productivity and profitability. Over the years, farming methods have evolved significantly, progressing from Agriculture 1.0, which relied on primitive tools, to Agriculture 2.0, which incorporated machinery and advanced farming practices, and subsequently to Agriculture 3.0, which emphasized mechanization and employed intelligent machinery and technology to enhance productivity levels. To further automate and increase agricultural productivity while minimizing agricultural inputs and pollutants, a new approach to agricultural management based on the concepts of the fourth industrial revolution is being embraced gradually. This approach is referred to as “Agriculture 4.0” and is mainly implemented through the use of Internet of Things (IoT) technologies, enabling the remote control of sensors and actuators and the efficient collection and transfer of data. In addition, fueled by technologies such as robotics, artificial intelligence, quantum sensing, and four-dimensional communication, a new form of smart agriculture, called “Agriculture 5.0,” is now emerging. Agriculture 5.0 can exploit the growing 5G network infrastructure as a basis. However, only 6G-IoT networks will be able to offer the technological advances that will allow the full expansion of Agriculture 5.0, as can be inferred from the relevant scientific literature and research. In this article, we first introduce the scope of Agriculture 5.0 as well as the key features and technologies that will be leveraged in the much-anticipated 6G-IoT communication systems. We then highlight the importance and influence of these developing technologies in the further advancement of smart agriculture and conclude with a discussion of future challenges and opportunities.

Postures anomaly tracking and prediction learning model over crowd data analytics.

Innovative technology and improvements in intelligent machinery, transportation facilities, emergency systems, and educational services define the modern era. It is difficult to comprehend the scenario, do crowd analysis, and observe persons. For e-learning-based multiobject tracking and predication framework for crowd data via multilayer perceptron, this article recommends an organized method that takes e-learning crowd-based type data as input, based on usual and abnormal actions and activities. After that, super pixel and fuzzy c mean, for features extraction, we used fused dense optical flow and gradient patches, and for multiobject tracking, we applied a compressive tracking algorithm and Taylor series predictive tracking approach. The next step is to find the mean, variance, speed, and frame occupancy utilized for trajectory extraction. To reduce data complexity and optimization, we applied T-distributed stochastic neighbor embedding (t-SNE). For predicting normal and abnormal action in e-learning-based crowd data, we used multilayer perceptron (MLP) to classify numerous classes. We used the three-crowd activity University of California San Diego, Department of Pediatrics (USCD-Ped), Shanghai tech, and Indian Institute of Technology Bombay (IITB) corridor datasets for experimental estimation based on human and nonhuman-based videos. We achieve a mean accuracy of 87.00%, USCD-Ped, Shanghai tech for 85.75%, and IITB corridor of 88.00% datasets.

Innovations for Interpretability, Flexibility, and Sustainability in Intelligent Machinery and Industry 4.0

Three levels, namely the device level, the connection level, and the systems management level, are frequently used to conceptualize intelligent machinery and Industry 4 [...]

A systematic review of IoT technologies and their constituents for smart and sustainable agriculture applications

Due to the world's rapid population expansion, the demand for food is anticipated to increase significantly during the coming decade. Traditional farming practices cannot meet the need for the food crop. Conventional farming methods use resources like land, water, herbicides, and fertilisers rather inefficiently. When it comes to making the most effective and sustainable use of resources to increase production, automation in agriculture is garnering a lot of interest. How people and machines operate on farms has been changed by integrating the Internet of Things (IoT) with numerous sensors, controllers, and communication protocols. A comprehensive literature review of the key technologies involved in smart and sustainable agriculture, viz. various sensors, controllers, communication standards, IoT based intelligent machinery, were compared and presented. These sensors were continuously producing a significant quantity of data on the agricultural field. These data were transmitted to the central control unit for analysis to meet the demands for water, fertiliser, pesticides, etc. The architecture and importance of data analytics in agriculture IoT, case studies of current agricultural automation utilising IoT, key challenges and open issues in agriculture IoT technology were discussed. The findings provide support for the selection of IoT technologies for specific applications.

Intelligent mechanical manufacturing technology based on intelligent manufacturing technology

With the development of intelligent manufacturing technology, the application of intelligent machinery manufacturing technology has been widely promoted. Intelligent machinery manufacturing technology is a technology that integrates advanced manufacturing technology and intelligent control technology. It can realize the intelligent control of the entire manufacturing process, improve the manufacturing efficiency and product quality, and reduce the manufacturing cost. Based on intelligent manufacturing technology, intelligent machinery manufacturing technology has been developed rapidly and has been widely applied in various fields such as automotive, aviation and so on. It can achieve the goals of automation, intelligence, high efficiency, and personalization, and promote the development of various industries towards intelligent manufacturing. However, there are still challenges in the development and application of intelligent machinery manufacturing technology, such as the high cost of equipment, the difficulty in controlling the manufacturing process, and the lack of skilled workers. Future research should focus on solving these challenges and promoting the further development and application of intelligent machinery manufacturing technology.

Federated Transfer Learning Method for Privacy-preserving Collaborative Intelligent Machinery Fault Diagnostics

Federated Transfer Learning Method for Privacy-preserving Collaborative Intelligent Machinery Fault Diagnostics

On Efficient Powder Manufacturing Using Process Control Methods and Cybernetics: A Background, Results and Policy Case Study

Powder manufacturing processes involve a systematic processing of particulate material towards forming a final product which needs to meet specific quality standards. A means towards ensuring that standards are met in a manufacturing plant is to apply self-regulating intelligent machinery that can take mitigatory actions in the presence of a process deviation or unsatisfactory process behavior. Given a case study of an open-loop batch powder agglomeration process, this work synthesizes the process of how a feedback element can be applied towards the powder manufacturing plant in order to allow for self-regulation and process optimization which, in turn, would increase process efficiency, minimize waste and ensure that the overall product quality meets production targets. Focusing on a proposed cybernetic loop with a feedback element, a vital component is the ability to measure key quality attributes of the powder mixture, which is crucial towards ensuring the control algorithm is able to implement the most reflective and appropriate control strategy on the powder process; thus, this work discusses heavily the sensing and associated signal processing component of the cybernetic loop. The results from the reviewed signal processing scenarios which comprised the in-process material spanned estimation accuracies of 3–10%, depending on the scenario that was considered. This paper also addresses the feared dystopian view of mass redundancy and job losses in the age of the introduction of enhanced automation technology within a manufacturing process, where it is distilled what kinds of knock-on effects can be expected as a result of the technology, in addition to means by which policy makers can contribute towards ensuring that the adaptation and transition process is as smooth as possible.