Intelligent Machinery Research Articles

Parallel collaborative planning for the coupled system of underground heavy-load robot

At present, designing and planning of robots are mainly based on path planning. This mode cannot meet requirements of real-time and precise planning for robots, especially under complex working conditions. Therefore, a parallel collaborative planning strategy is proposed in this paper, which parallel collaborates optimal task allocation planning and optimal local path planning. That is, according to real-time dynamic working environment of robots, the dynamic optimal task allocation planning strategy for coupled system of robot in low coupling state is adopted, to improve real-time working efficiency of underground heavy-load robot. Meanwhile, the parallel elite particle swarm optimization algorithm is adopted to improve accuracy of path tracking and controlling. Finally, the two planning strategies are collaborated parallel to realize intelligent and efficient planning of whole complex coupled system for underground heavy-load robot. The simulation and experiment results show that the parallel collaborative planning algorithm proposed in this paper has perfect controlling effects: Total flow of overall system is saved by 11.03 L, execution time saved by 16.8 s and implementation efficiency has been improved by 10 times. Therefore, the parallel collaborative planning strategy proposed in this paper can not only meet requirements of high efficiency and precision of intelligent robot under complex working conditions, but also greatly improve real-time working effectiveness and robustness of robots, so as to provide a reference for dynamic planning of complex intelligent engineering machinery, and also supply design basis for development of multi-robot collaborative system.

Future Ceiling Design Based on Fischer

This design cleverly integrates modern machinery and control technology into the home system, and cleverly combines intelligent machinery and intelligent life, making family life more intelligent, comfortable and convenient. Use the ceiling as the carrier to realize the three-dimensional home design, make full use of the indoor top space, and solve the problems of small residential space and difficult storage. The main functions contained in this device are stored indoors. The introduction of this design conforms to the trend of intelligent family life and greatly facilitates people’s daily life.

A two-way linear piezoelectric vibratory conveyor actuated by a composite sinusoidal voltage input

Piezoelectric vibratory conveyors are widely used in many industrial fields such as factory automation. However, there is still a limitation for most cases that the vibratory conveyor can only feed in the designed direction. In today's multitasking intelligent machinery, how to sort different parts from the same feed source and convey them efficiently in different directions remains a challenge to be overcome. This study proposes an innovative design that uses a composite sinusoidal control signal to drive a linear two-stage piezoelectric vibratory feeder. By simply tuning the amplitude and phase of the input composite signal, the conveying velocity and direction can be altered easily. The dynamic characteristics of this feeder were both investigated by theoretical formulation and experimental measurement. It is found that the conveying performance of this feeder is better suited for using the composite signal with two sinusoidal components of double frequency. With the composite control signal synthesized by two sinusoidal components of 237.5 Hz and 475.0 Hz, which were close to the feeder’s first two resonance frequencies to increase the response gain of the structure, and the input voltage of 60 V, which corresponded to the trough’s displacement amplitude of 54.5 m, the measured conveying speeds were +30.8 mm/s and –33.3 mm/s for setting the phase angles 180° and 0°, respectively. The result demonstrates the feasibility of the proposed two-way vibratory feeder design.

Intelligent Automatic 3D Printing Machine Based on Wireless Network Communication

With the advent of the era of big data, people have higher requirements for material and spiritual gains and happiness, and their demands for various services in the communications field are diversified. The most significant change is the 3D printing technology. With its unique advantages, 3D printing is gradually sweeping the world. In order to explore whether the technology or digital model based on wireless network communication can realize the automation and intelligence of 3D printing, this article applies a variety of scientific methods such as simulation experiment method, data collection method, and sample analysis method; collects samples; and simplifies the algorithm, using basic experimental methods, orthogonal experiments, and single‐factor experiments to study the various influencing factors in the automatic printing process to obtain the optimal parameter combination. Experimental results show that with the support of wireless communication network hardware and data, the timeliness and quality of 3D printing can be significantly improved. These are the basis for realizing 3D printing automation and intelligence. Further experiments show that the wireless communication network intelligent automatic 3D printing machinery adjusts 3D printing parameters through wireless communication technology, combined with intelligent real‐time monitoring and sensing equipment; we can find that the printing efficiency of the 3D printing machinery is increased by about 15%, and the economic cost is saved by about 20%. It basically shows the practicality of the experimental research results.

Knowledge representation and decoupling analysis on failure mechanisms of remotely controlled intelligent machinery

Remotely controlled intelligent machinery has complications, including loose management of failure information, low information availability, and coupling influence among systems, which can be effectively solved by analyzing the system state and information characteristics of the equipment. Taking intelligent agricultural machinery as the object, this study applies the knowledge representation method to explore equipment failure states' informational features and construct a knowledge framework model of system failure representation relations and a complex network conceptual model to visualize the failure information more intuitively and facilitate systematic management and utilization. The feedback-based decoupling analysis method uncouples the coupling between subsystems, identifying the critical state of decoupling well. It attempts to apply the knowledge representation and decoupling analysis to remotely controlled intelligent agricultural machinery equipment. Through the example, the result further illustrates the feasibility of knowledge representation and decoupling for remotely controlled intelligent agricultural machinery systems and provides essential support for better failure analysis.

An intelligent framework for modelling and simulation of artificial neural networks (ANNs) based on augmented reality

The digitalization of industry is targeting at the integration of artificial intelligence (AI) in manufacturing systems, for delivering intelligent machinery. Although AI seems a long-term target, similar enabling technologies such as artificial neural networks (ANNs) have been introduced. Despite that ANNs are inspired by the human brain’s functioning, understanding how they work and training them is a challenging task, requiring engineers with advanced math and coding skills. On the contrary, augmented reality (AR) is a cutting-edge digital technology, enabling the registration of 3D content on the physical environment, thus enhancing user’s perception in a growing variety of scientific fields. Therefore, this research work aims at the design and development of an AR-based framework that facilitates the conceptualization of an ANN through AR, assists engineers train efficient ANN and moreover share knowledge through suitable communication channels. Finally, the framework can handle datasets with the use of cloud services.

Cutting duration and performance parameters of a harvester\u2019s sawing unit under real working conditions

Amongst all the working elements of single grip harvesters, the working elements of felling and processing play an important role within the cut-to-length (CTL) harvester’s working phases. This includes the felling cutting and cross-cutting of stems within the felling and processing operation. The detailed investigation of such individual machine activities may help to analyse and improve the performance of forest machines. The objective of this study was to investigate the cutting duration and other performance parameters, including the fuel consumption, of a CTL harvester’s sawing unit under real working conditions. Detailed information on the felling cutting and cross-cutting performance was collected at short intervals using CAN bus data of two single grip harvesters in final felling from two different sites in Eastern Finland. As a result, models for effective time consumption in the work phase of cutting as a function of stem size were developed, both for felling cutting and cross-cutting. Felling cutting and cross-cutting durations were somewhat identical until the cutting diameters of 400–450 mm, depending on the site. Thereafter, the cutting time difference increased and was higher in felling cutting. At the site with large diameters of 550–650 mm, the difference varied between 15% and 28%, between the comparisons of formulae. In addition, other performance parameters, including the respective fuel consumption of this working phase, were part of the study. The study revealed a higher hourly based fuel consumption for the entire guide bar movement time compared to the pure cutting time in cross-cutting with stem diameters below 400 mm. Detailed knowledge of the performance of the sawing unit’s activities might help the planning process of future studies, as well as support the future development of efficient and intelligent machinery.

Domain generalization in rotating machinery fault diagnostics using deep neural networks

The past years have witnessed the successful development of intelligent machinery fault diagnostic methods. Besides the basic data-driven fault diagnosis tasks where the training and testing data are collected from the same distribution, the more practical cross-domain problems have also attracted much attention considering variations of machine operating conditions. In the literature, most existing studies generally assume the availability of testing data during model training, thus facilitating explicit domain adaptations. However, this assumption poses obstacles in the application on real-time cross-domain fault diagnosis, where the testing data can not be obtained in advance. This paper proposes a deep learning-based domain generalization method for machinery fault diagnosis. A domain augmentation method is adopted to expand the available dataset. Domain adversarial training is implemented, and generalized features can be learned from different domains, which hold in new working scenarios without assuming the availability of testing data. Distance metric learning is also used to further enhance model robustness in fault classifications. Through experiments on two rotating machinery datasets, the effectiveness of the proposed method is validated, which is promising in on-line cross-domain fault diagnosis tasks.

Factors Affecting the Adoption of Agricultural Automation Using Davis’s Acceptance Model (Case Study: Ardabil)

Abstract Taking into account that traditional agricultural methods reduce the farm performance and make agriculture economically ineffective, development of intelligent machinery is essential for improving the quality of crops and agricultural activities. The most important issue in development of agricultural technologies lies in users’ willingness to adopt it. Therefore, the purpose of this study is to investigate the key factors of Davis’s model in automation acceptance in agriculture. Presented paper describes an applied research with a survey approach through a questionnaire. Questionnaire data were collected from 378 people and respondents include university students, farmers and experts in a randomized sampling from the Ministry of Agriculture. Firstly, the questionnaire data were described in a form of statistical numerical characteristics. Secondly, in order to verify the data normality, the Kolmogorov-Smirnov test was calculated in SPSS and the relationship between the variables was investigated in the conceptual model. Subsequently, hypotheses were tested via appropriate statistical models using LISREL and SPSS software. The results showed that for all hypotheses, the T-test exceeds 1.96 and the significance level is less than 0.05. In such a manner, all hypotheses were confirmed at 95% level, and the path coefficients in the hypotheses H1, H2 and H6 were negative – indicating the negative effect of the independent variable on the dependent variable in the hypothesis. In the other hypotheses, these were positive – indicating the positive effect of the independent variable on dependent variable. By means of modelling, it was found that there was an inverse relationship between social and individual factors with perceived usefulness, as well as an inverse relationship between social factors with perceived ease-of-use, while there was a positive and significant relationship between other factors. According to a set of fitting indices, the research conceptual model was appropriate and Cronbach’s coefficients for each factor were greater than 0.7, suggesting that the questionnaire was valid. On the basis of findings, the better a person understands the usefulness of automation, the more likely this person is to adopt it. Since the risk and issues related to it play an important role in farmers’ decision making, it is recommended that future studies address the issue of risk in adopting precision farming technology.

부산의 제5차 전략산업의 특화도와 성장기여도 분석

This paper analyzes the specialization and growth contribution of the fifth strategic industries in Busan using the recent 2007-2016 statistical industry data. To do this, this paper adopted the location quotient and shift-share analysis in seven strategic industries which were selected by Busan City Government in 2019. Busan’s strategic industries have comparative advantages in the smart marine industry which includes environment friendly smart industry and marine bio-industry, intelligent machinery industry, and intelligent information service industry especially in smart financial industry. The other industries seem not to have comparative advantages. According to analysis on growth contribution of the fifth strategic industries, the marine bio-industry of smart marine industry, intelligent machinery industry, airplane, life care industry, and global tourism and MICE industry have the positive effects of regional specialization.

Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation

Recently, due to the rising industrial demands for intelligent machinery fault diagnosis with strong generalization, transfer learning techniques have been used to enhance adaptability of data-driven approaches. Particularly, the domain shift problem where training and testing data are sampled from different operating conditions of the same machine is well addressed. However, it is still difficult to prepare sufficient labeled data on the tested machine. Therefore, the idea of transferring fault diagnosis knowledge learned from one machine to different but related machines is motivated, and that is realized through a deep learning-based method in this paper. Features of different equipments are first projected into the same subspace using an auto-encoder structure, and cross-machine adaptation algorithm is adopted for knowledge generalization, where the distribution discrepancy between data from different machines is minimized. Experiments on three rolling bearing datasets are implemented to validate the proposed method. The results suggest it is feasible to transfer fault diagnosis knowledge across different machines, and the proposed method offers a novel and promising approach for knowledge generalization.

Exploration and Application of Machine Learning Technology Based on Swarm Intelligence Optimization

Background: In the agriculture development and growth, the intelligent machinery and equipment plays an important role. Various researchers are involved for implementing the research and patents to aid the smart agriculture and author reviewer that machine leaning technologies are providing the best support for this growth. Method: To explore machine learning technology and machine learning algorithms, mostly based on swarm intelligence optimization, and their applications are studied. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analysed and compared in detail with other existing algorithms. Results: The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. Conclusion: The model and algorithm helped realise a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.

Intelligent construction technology of railway engineering in China

Intelligent construction technology has been widely used in the field of railway engineering. This work first analyzes the connotation, function, and characteristics of intelligent construction of railway engineering (ICRE) and establishes its system structure from three dimensions, namely, life cycle, layers of management, and intelligent function, to deeply understand the development situation of intelligent railway construction in China. Second, seven key technical support systems of ICRE, which include building information modeling (BIM) standard system for China’s railway sector, technology management platform and life cycle management based on BIM + GIS (geography information system), ubiquitous intelligent perception system, intelligent Internet-of-Things (IoT) communication system based on mobile interconnection, construction management platform based on cloud computing and big data, unmanned operation system based on artificial intelligence, intelligent machinery and robot, and intelligent operation and maintenance system based on BIM and PHM (prediction and health management), are established. Third, ICRE is divided into three development stages: primary (perception), intermediate (substitution), and advanced (intelligence). The evaluation index system of each stage is provided from the aspects of technology and function. Finally, this work summarizes and analyzes the application situation of ICRE in the entire railway sector of China, represented by Beijing-Zhangjiakou and Beijing-Xiong’an high-speed railways. Result shows that the technical support systems of the ICRE have emerged in China and are still in the process of deepening basic technology research and preliminary application. In the future, the ICRE of China’s railway sector will develop toward a higher stage.

RETRACTED ARTICLE: Research on application of the Internet of things technology in financial leasing of intelligent manufacturing enterprises

In 2015, the State Council issued “Made in China 2025,” stating that the deep integration of information technology and manufacturing should be accelerated, and intelligent manufacturing should be the main direction of integration. In this context, China’s manufacturing-led cities have implemented the “machine substitution” program to upgrade traditional machinery and equipment to intelligent machinery and equipment, which has led to the development of traditional financial leasing models in China, such as capital. Constraints, financing channel constraints, risk constraints, and information constraints have severely constrained the advancement of the intelligent manufacturing process. In this context, the paper discusses the use of the Internet of things as a technical means to help the existing smart manufacturing enterprises to access the financing channels and open up new financing channels.

Unmanned Ground Vehicle Modelling in Gazebo/ROS-Based Environments

The fusion of different technologies is the base of the fourth industrial revolution. Companies are encouraged to integrate new tools in their production processes in order to improve working conditions and increase productivity and production quality. The integration between information, communication technologies and industrial automation can create highly flexible production models for products and services that can be customized through real-time interactions between consumer, production and machinery throughout the production process. The future of production, therefore, depends on increasingly intelligent machinery through the use of digital systems. The key elements for future integrated devices are intelligent systems and machines, based on human–machine interaction and information sharing. To do so, the implementation of shared languages that allow different systems to dialogue in a simple way is necessary. In this perspective, the use of advanced prototyping tools like Open-Source programming systems, the development of more detailed multibody models through the use of CAD software and the use of self-learning techniques will allow for developing a new class of machines capable of revolutionizing our companies. The purpose of this paper is to present a waypoint navigation activity of a custom Wheeled Mobile Robot (WMR) in an available simulated 3D indoor environment by using the Gazebo simulator. Gazebo was developed in 2002 at the University of Southern California. The idea was to create a high-fidelity simulator that gave the possibility to simulate robots in outdoor environments under various conditions. In particular, we wanted to test the high-performance physics Open Dynamics Engine (ODE) and the sensors feature present in Gazebo for prototype development activities. This choice was made for the possibility of emulating not only the system under analysis, but also the world in which the robot will operate. Furthermore, the integration tools available with Solidworks and Matlab-Simulink, well known commercial platforms of modelling and robotics control respectively, are also explored.

Deep forest based intelligent fault diagnosis of hydraulic turbine

Deep neural networks (DNNs) for intelligent machinery fault diagnosis require a large amount of training data, powerful computational facilities and have many hyper-parameters that have to be carefully tuned to ensure maximum performance. Deep forest, as a novel alternative to the deep learning framework, has the potential to overcome these shortcomings. In this study, a deep forest-based end-to-end intelligent fault diagnosis method is proposed for hydraulic turbine, in which multi-grained scanning is first used to transform fault feature representations from raw data and enhance fault feature learning ability, and then cascade structure is constructed with different types of random forests to learn fault features level by level and classify faults. The effectiveness of the proposed method is validated using the experimental dataset under twelve conditions, and its practicability is validated using a simulated dataset generated by adding white Gaussian noise to raw experimental signals. The results show that the proposed method is able to adaptively mine available fault features from measured signals, and its diagnosis accuracy is better than that obtained by existing methods. More importantly, the proposed method has better robustness to noise and is less limited to the number of training data.

Feature selection tree for automated machinery fault diagnosis

Intelligent machinery fault diagnosis commonly utilises statistical features of sensor signals as the inputs for its machine learning algorithm. Due to the abundance of statistical features that can be extracted from raw signals and the accuracy of inserting all the available features into the machine learning algorithm for machinery fault classification, less accurate fault classification may inadvertently result due to overfitting issues. It is therefore only by selecting the most representative features that overfitting outcomes can be avoided and classification accuracy be improved. Currently, the genetic algorithm (GA) is regarded as the most commonly used and reliable feature selection tool for the improvement of accuracy for any machine learning algorithm. However, the greatest challenge for GA is that it may fall into a local optima and be computationally demanding. To overcome this limitation, a feature selection tree (FST) is here proposed. Numerous experimental dataset feature selections were executed using FST and GA; their performance is compared and discussed. Analysis showed that the proposed FST resulted in identical or superior optimal feature subsets when compared to the renowned GA method, but with a 20-time faster simulation period. The proposed FST is therefore more efficient in performing feature selection task than GA.

Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation

Intelligent machinery fault diagnosis system has been receiving increasing attention recently due to the potential large benefits of maintenance cost reduction, enhanced operation safety and reliability. This paper proposes a novel deep learning method for rotating machinery fault diagnosis. Since accurately labeled data are usually difficult to obtain in real industries, data augmentation techniques are proposed to artificially create additional valid samples for model training, and the proposed method manages to achieve high diagnosis accuracy with small original training dataset. Two augmentation methods are investigated including sample-based and dataset-based methods, and five augmentation techniques are considered in general, i.e. additional Gaussian noise, masking noise, signal translation, amplitude shifting and time stretching. The effectiveness of the proposed method is validated by carrying out experiments on two popular rolling bearing datasets. Fairly high diagnosis accuracy up to 99.9% can be obtained using limited training data. By comparing with the latest advanced researches on the same datasets, the superiority of the proposed method is demonstrated. Furthermore, the diagnostic performance of the deep neural network is extensively evaluated with respect to data augmentation strength, network depth and so forth. The results of this study suggest that the proposed intelligent fault diagnosis method offers a new and promising approach.

Automation and employment: The case of South Africa

In the two centuries since the Industrial Revolution technological progress has had a major impact on the types of work humans perform. The invention of increasingly advanced machinery decreases, on one hand, the need for certain forms of manual labour while, on the other, creating new needs and new types of work. Through continuous cycles of this process advanced industries have emerged enabling standards of living to rise across the world. The most recent wave of technological progress is characterized by increasingly intelligent computers and computer-driven machinery. This has coincided with the rise of economic inequality in previously egalitarian countries, prompting debate over the implications of the computer revolution for low and medium-skilled workers. In this study focus falls on the possible implications of these developments for the South African labour market. By using an oft-cited index of occupation computerization probabilities in combination with Stats SA labour market data, a future outlook is determined and presented. Findings suggest that the occupations performed by almost 35% of South African workers (roughly 4.5 million people) are potentially automatable in the near future.

3D pulsed chaos lidar system

We develop an unprecedented 3D pulsed chaos lidar system for potential intelligent machinery applications. Benefited from the random nature of the chaos, conventional CW chaos lidars already possess excellent anti-jamming and anti-interference capabilities and have no range ambiguity. In our system, we further employ self-homodyning and time gating to generate a pulsed homodyned chaos to boost the energy-utilization efficiency. Compared to the original chaos, we show that the pulsed homodyned chaos improves the detection SNR by more than 20 dB. With a sampling rate of just 1.25 GS/s that has a native sampling spacing of 12 cm, we successfully achieve millimeter-level accuracy and precision in ranging. Compared with two commercial lidars tested side-by-side, namely the pulsed Spectroscan and the random-modulation continuous-wave Lidar-lite, the pulsed chaos lidar that is in compliance with the class-1 eye-safe regulation shows significantly better precision and a much longer detection range up to 100 m. Moreover, by employing a 2-axis MEMS mirror for active laser scanning, we also demonstrate real-time 3D imaging with errors of less than 4 mm in depth.