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The expansion of services and technological equipment applied to the agribusiness sector grows year after year, e.g., tractors and agricultural machinery, which use systems shipped with sophisticated software that collaborate to aid, and optimize activities in the field. Maintenance of agricultural machinery, including tractors, is routine in the l...
Context in source publication
Context 1
... to the manufacturer, the T8.270 features easy-access maintenance points and longer maintenance intervals [27], which clearly demonstrates that it is a line of tractors for large-scale productivity, and pressure data of the clutch system used in this work. The transmission system (Full Powershift (18 × 4)) of the T8.270 is fully automatic and hydraulic (illustrated in Figure 3). The Full Powershift (18 × 4) transmission combines its proven efficiency with the ease of operation [28]. ...
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In Cuba, the rice harvest is an operation that is carried out with the help of agricultural machinery, which represents strong investments. For this reason, the dynamics of the production process requires that the harvesting machines be in perfect technical condition during this period, because of that it is necessary to properly organize and plan...
Citations
... Damage that can be seen on a single machine; It also causes other machines to malfunction. In the studies, maintenance planning problems have been discussed from different angles in different sectors (Barabady and Kumar, 2008;Gu and Huang, 2010;Bose et al., 2012;Jurca, 2012;Poozesh et al., 2012;Lynch et al., 2013;Khodabakhshian, 2013;Afsharnia et al., 2014;Amini Khoshalan et al., 2015;Najafi et al., 2015;Obinna and Oluka, 2016;Wolfert et al., 2017;Rybacki and Grześ, 2018;Da Silva et al., 2019). ...
Productivity is one of the most important measures used to determine the growth and development level of countries or sectors. A wide variety of projects have been planned and implemented to increase agricultural productivity. The productivity to be obtained in agriculture; Soil conditions, climate, seeds, fertilizer, pesticides, labor and agricultural mechanization directly affect it. Agricultural mechanization is the realization of agricultural activities by using energy together with agricultural tools and machines. Agricultural mechanization; It is an important agricultural production technology that helps increase agricultural productivity. Due to the inadequate maintenance planning of agricultural machinery, agricultural machinery cannot be utilized at the desired level in agricultural production. Most agricultural equipment is subject to frequent changes in speed and direction of movement while operating. Damage that can be seen on a single machine; It also causes other machines to malfunction. During the year, especially in the months when agricultural activity is high, excessive working tempo can cause tractors to malfunction. The breakdown of tractors causes disruptions in agricultural activities. In addition, the breakdown of tractors increases the repair costs. Since there is no tractor maintenance planning, farmers face interruptions in agricultural activities due to tractor malfunction. However, tractor malfunctions may cause cost and economic losses. For these reasons, there is a need for appropriate maintenance planning of agricultural machinery in order to continue agricultural activities without disruption. Maintenance planning; It consists of a set of preventive activities to improve the reliability and availability of any system. The main purpose of this study is to determine and rank the importance level weights of the criteria that are important for agricultural machinery maintenance planning using the fuzzy AHP method. Fuzzy AHP method, which provides ease of application, was preferred in determining the Criterion Weights. The research proposes a framework to determine the weights of appropriate criteria for care planning selection through a combined approach of fuzzy multi-criteria decision making involving relevant stakeholders. On the basis of the prioritization of criteria of tractor maintenance planning (TMP), it was found from the ranking that checking for all fluid levels (TMP1) ranked first. This respectively is followed by checking for general conditions (TMP4), checking for tires and wheels (TMP2) and checking for batteries (TMP3). With the results of the study, a guide was created for farmers and other stakeholders, as well as decision makers, to help plan the maintenance of machines in better working conditions. It is also thought that this study will be encouraging for other studies.
... In the field of software engineering, deeply understanding the structure and meaning of codebases is a crucial but troublesome task for programmers [1,2]. This not only adds an extra burden on them but also increases the monotony of programming work [3,4]. While existing methods like dependency graphs, UML (Unified Modeling Language) views and code annotations provide a foundation for understanding code structure [5,6], they are limited in terms of intuitiveness and comprehensibility. ...
In the field of software engineering, large and complex code bases may lead to some burden of understanding their structure and meaning for developers. To reduce the burden on developers, we consider a code base visualization method to visually express the meaning of code bases. Inspired by remote sensing imagery, we employ graphical representations to illustrate the semantic connections within Java code bases, aiming to help developers understand its meaning and logic. This approach is segmented into three distinct levels of analysis. First, at the project-level, we visualize Java projects by portraying each file as an element within a code forest, offering a broad overview of the project’s structure. This macro-view perspective aids in swiftly grasping the project’s layout and hierarchy. Second, at the file-level, we concentrate on individual files, using visualization techniques to highlight their unique attributes and complexities. This perspective enables a deeper understanding of each file’s structure and its role within the larger project. Finally, at the component-level, our focus shifts to the detailed analysis of Java methods and classes. We examine these components for complexity and other specific characteristics, providing insights that are crucial for the optimization of code and the enhancement of software quality. By integrating remote sensing technology, our method offers software engineers deeper insights into code quality, significantly enhancing the software development lifecycle and its outcomes.
... In general, maintenance is considered as routine for the farmer, which means that preventive and corrective maintenance is the standard procedure on farms (Da Silva et al. 2019). In a previous study, Regler et al. (2023) calculated the possible savings of labor time and usable input compared to "maintaining a PTO shaft before every usage" or "completely omitting maintenance" to "maintaining a PTO shaft when the manufacturer recommends to". ...
... In a previous study, Regler et al. (2023) calculated the possible savings of labor time and usable input compared to "maintaining a PTO shaft before every usage" or "completely omitting maintenance" to "maintaining a PTO shaft when the manufacturer recommends to". They came to a comparable result as Da Silva et al. (2019) and achieved an average labor time reduction of 81.8% and a cost reduction of up to 93.8%. An additional advantage of the operation hours analysis is the actual machine time detection, which gives information about the time in use of every implement, no matter if the implement is already digitized and modern, an older, or a simple mechanic implement like a rotary harrow or a rotary cultivator. ...
In agriculture, maintenance is one of the most important tasks to keep machinery reliable and downtimes low. Specific maintenance plans exist for most modern machines and implements with defined frequencies and tasks from their manufacturers. One part that often got missed out is the agricultural PTO shaft, which is not directly a part of the machine, neither of the implement. Even though, omitted or reduced maintenance of the PTO shaft leads to downtimes of the production.
In order to perform maintenance correctly, accurate maintenance instructions need to be available. Digitization can solve the problem of the PTO shaft’s unique standing, as operation hours are retraced precisely by a sensor, and the right moment for maintenance is calculated. Commonly, the PTO shaft is maintained together with the implement it is attached to, and its specific maintenance intervals are ignored, even though the manufacturer gives those intervals. Here, the point of view needs to be shifted to the uniqueness of the PTO shaft and its recommended maintenance intervals.
This work summarizes the chances and threats of the actual situation of PTO shaft maintenance and introduces an operation hours counter to estimate the right moment for PTO shaft maintenance in combination with a smartphone application. Ultimately, operation hours detection of the PTO shaft gives the opportunity to directly measure the working time of the attached implement. Thereby, older and pure mechanical implements can be digitized, but likewise modern implements benefit from optimized maintenance.
... We carefully investigated the study to detect the features implemented by (da Silva et al., 2019). As they used a PicoScope6 device to perform the data acquisition and predictive maintenance, we chose Batch acquisition and analysis. ...
... We created a context diagram based on the available information explicitly mentioned in da Silva et al. (2019). There were no entities to add. ...
... The case studies for the derivation of AAs were based on three peerreviewed articles (Cohen & Singer, 2021;da Silva et al., 2019;Yu et al., 2021). Although they did not always explicitly name entities, features, or deployment environments, the studies contained sufficient detail to derive the diagrams and views. ...
Context
Digital Twin-based predictive maintenance systems are frequently integrated into complex systems. The success of the integration depends on the design of the system. A Reference Architecture can be used as a blueprint to design Application Architectures rapidly and consistently for various application domains, resulting in a reduced time-to-market.
Objective
The main objective of this study is to develop and evaluate a Reference Architecture designed using renowned software architecture methods.
Method
A domain analysis was performed to gather and synthesize the literature on Digital Twin-based predictive maintenance systems, which we used to model the key features. We applied UML diagrams to design the reference architecture based on the feature model. We evaluated the reference architecture using three case studies.
Results
We derived three views for Digital Twin-based predictive maintenance systems. For the user's view, we developed a context diagram. We developed a package diagram for the structural view, and we designed a layered view to show the system's decomposition in layers. We designed an Application Architecture for each case study based on the study's features using each Reference Architecture view. Additionally, we designed a deployment view to describe the hardware and software and its environment.
Conclusion
We demonstrated that the methods of creating a Reference Architecture could be used in the Digital Twin-based predictive maintenance domain and showed how an Application Architecture could be designed in this context.
... For example, at the operational level numerous failure analysis techniques have been developed to predict the degradation trends of agricultural fleets. Different types of advanced knowledge and data mining techniques have been utilized in this direction to address the issues of uncertainty and variability (da Silva et al. 2019;Naji et al. 2019;Kumar et al. 2019;Kurochkin et al. 2017;Hu et al. 2015;Jong et al. 2013). Moreover, performance measurement and continuous improvement (Soltanali et al. 2020;Parida and Kumar 2006;Tsarouhas 2007), reliability and availability assessment (Soltanali et al. 2022;Mishra and Satapathy 2021;Afsharnia et al. 2020), impact of information technology (Patel and Sayyed 2014;Sørensen and Bochtis 2010), maintenance cost modeling (Rohani et al. 2011;Lips and Burose 2012)., etc. have been studied in food and agriculture sectors which principally correspond to the tactical and operational supplies of the maintenance model. ...
A well-established maintenance management system is key in improving the operational performance within agricultural production systems. In this paper, we investigated the major criteria influencing effective maintenance management in agro-industries. To that end, we started by presenting a hierarchical structure of criteria and their Critical Success Factors (CSFs) after reviewing related studies and dividing the criteria into the categories of organization management, human-related, and organizational aspects. To assess the weight of the criteria and their cause-effect relationship, we collected the opinions of maintenance experts working in different agro-industries in Iran, using several online questionnaires which were based on Multi-criteria decision-making (MCDM) techniques such as Best-Worst Method (BWM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL). The results of the BWM revealed that top management support, and fund allocation and inventory resource management are the most important CSFs in the proposed maintenance model with the global weights of 0.108 and 0.075, respectively. According to the DEMATEL, five CSFs such as top management support, training and education, fund allocation and inventory resource management, maintenance strategies and policies, and adequacy of the maintenance crew, were recognized as causal variables of maintenance management within Iranian agro-industries. The proposed methodology in this paper could help agro-industries in ensuring an effective maintenance management system.
... Today, there are several different ways to determine the right intervals for maintenance of machines to minimize downtime. For example, there are systems which use predictive software related to the clutch of the tractor [8], changes in the quality of engine oils [9], or vibration analysis of the machine [10]. Equally, there are a large number of studies about the necessity of proper lubrication of agricultural tractors to ensure their proper functionality [11]. ...
Power take-off (PTO) shafts are widely used in agricultural machinery to directly power implements with the tractor’s engine. While the operating conditions such as dirt, weather, peak loads and utilization greatly influence a PTO shaft’s lifetime, proper maintenance, especially lubrication, is necessary for an appropriate operation and maximum lifetime. Hence, this paper evaluates the potential for saving resources during maintenance by using a digital maintenance assistant. To simulate and study the cost of PTO shafts for agricultural implements, an investigation of the lubrication process of PTO shafts was carried out on several test farms. In practice, two extremes are common: Either far too much grease is used for best possible lubrication, or the PTO shaft is not lubricated at all. To simulate the annual cost of maintenance, three scenarios were calculated and compared: “Maintenance before usage” and “no maintenance” as extreme scenarios and “maintenance according to the manufacturer’s specifications” by a digital maintenance assistant as an optimum scenario. The simulation of different maintenance scenarios shows a reduction in maintenance costs of up to 93.8%, an average reduction of lubricants by 86.3% and an average labor time reduction by 81.8% for the case of “maintenance according to the manufacturer’s specifications”. The results of this work show that there is potential for major savings using a digital maintenance assistant. This development could be the first step towards the digitalization of maintenance activities on agricultural farms.
... Agricultural mechanization is an important factor in increasing the productivity of agricultural production and the quality development of agriculture [2]. In addition, the low supply of labor in agriculture requires that agricultural activities should be carried out using agricultural machinery [3]. Much attention should be paid to the selection of appropriate machinery in agriculture [4]. ...
To achieve the highest possible agricultural production, it is necessary to procure the appropriate agricultural machinery. A tractor is the most useful machine in agriculture that performs various functions. Therefore, the selection of a tractor is one of the key decisions in the agriculture-production process. This study aims to evaluate heavy tractors for agricultural production in Bosnia and Herzegovina. Since this is a selection between different tractors, which are evaluated using several criteria, the methods of multi-criteria analysis (MCDA) were used in this study. Five different methods were used to determine the weight of the criteria, of which a modified standard-deviation method is a new method used in practice, while the tractor ranking was performed using the CRADIS (compromise ranking of alternatives from distance to ideal solution) method. The results showed that the best-ranked tractor is A4, while the most deviations from the ranking occur when the entropy method is used. The contribution of this study is in the systematization of the methods for the objective determination of the criteria weights and the development of new methods to facilitate decision-making in agriculture and other industries.
... The relations of working parameters of combine harvesters present multiple possible causes or one cause with various results. Different researchers have studied the key parts of the combine harvester [25]- [27]. To conduct correlation calculations, the independent time series characteristics of key components like speed, temperature, and engine working index, are selected. ...
Cooling water temperature of the combine harvester during operations can reflect the changes of its power consumption and even overloads caused by extreme workload. There is an existing problem when extracting water temperature information from harvesters: data redundancy and the loss of time series feature. To solve such problem, a Convolutional denoising autoencoder and Long-Short Term Memory Artificial Neural Network (CDAE-LSTM) hybrid model based on parameter migration is proposed to predict temperature trends. Firstly, the historical data of the combine harvester are taken into account to perform correlation analysis to verify the input rationality of the proposed model. Secondly, pre-training has been performed to determine the model’s initial migration parameters, along with the adoption of CDAE to denoise and reconstruct the input data. Finally, after the migration, the CNN-LSTM hybrid model was trained with a real dataset and was able to predict the cooling water temperature. The accuracy of the model has been verified by field test data gathered in June 2019. Results show that the root mean squared error (RMSE) of the model is 0.0817, and the mean absolute error (MAE) is 0.0989. Compared with the performance of LSTM on the prediction data, the RMSE improvement rate is 2.272 %, and the MAE improvement rate is 20.113 %. It is proven that the adoption of CDAE stabilizes the model, and the CDAE-LSTM hybrid model shows higher accuracy and lower uncertainty for time series prediction.
... Various studies have been conducted through field measuring in the agricultural machinery field, including fault diagnosis, monitoring of working performance, and the development of decision-support system for agricultural machinery [8][9][10][11]. In another study, a field measuring of hydraulic pressure during was conducted to diagnose failures in the transmission system of tractor [12]; the results showed an 88% reduction of repair time and a 93% reduction of repair cost. In another study, a decision-support system was developed and applied to assist producers with expected yield improvements using a global positioning system [13]; a suitable agricultural machinery matching with the working area through an application of the development system, it was found that the carbon emission was reduced to improve the working environment, and fuel efficiency was increased to reduce operating costs. ...
This study was conducted to analyze the effects of tillage depth and gear selection on the mechanical load and fuel efficiency of an agricultural tractor during plow tillage. In order to analyze these effects, we developed an agricultural field measuring system consisting of a load measurement part (wheel torque meter, proximity sensor, and real-time kinematic (RTK) global positioning system (GPS)) and a tillage depth measurement part (linear potentiometer and inclinometer). Field tests were carried out using moldboard plows with a maximum tillage depth of 20 cm and three gear selections (M2H, M3L, and M3H) in a rice stubble paddy field for plow tillage. The average travel speed and slip ratio had the lowest M2H and the highest M3L. M3H had the highest theoretical speed, but the travel speed was 0.13 km/h lower than M3L due to the reduction in the axle rotational speed at deep tillage depth. Regarding engine load, the higher the gear, the greater the torque and the lower the axle rotation speed. The front axle load was not significantly affected by the tillage depth as compared to other mechanical parts, except for the M3H gear. The rear axle load generated about twice the torque of the front wheel and overall, it tended to show a higher average rear axle torque at higher gear selection. The rear axle load and fuel rate were found to be most affected by the combination of the tillage depth and gear selection combination. Overall, field test results show that the M3H had the highest fuel efficiency and a high working speed while overcoming high loads at the same tillage depth. In conclusion, M3H is the most suitable gear stage for plow cultivation, and the higher the gear stage and the deeper the tillage depth during plowing, the higher the fuel efficiency. The results of this study will be useful for analyzing mechanical load and fuel efficiency during farm operations. In a future study, we will conduct load analysis studies in other farming operations that consider various soil mechanics factors as well as tillage depths and gear selections.
With the rapid development of China's economy, all kinds of machinery and equipment in the industrial field are developing in the direction of high concentration and refinement. The precise cooperation between a variety of mechanical equipment makes the entire mechanical system run safely and smoothly. Therefore, the importance of the safe operation of each equipment is self-evident. The purpose of this paper is to study the application of decision tree algorithm(DTA) in machinery equipment fault diagnosis(FD) system. The analysis principle and construction process of the DTA are introduced. On this basis, the optimization of the DTA model is proposed. Tested on the Weka machine learning platform, compared with the traditional ID3 decision tree (DT) construction algorithm, the DT structure constructed by the algorithm in this paper is simple, which improves the generalization ability of the DT, and also has a certain ability to suppress noise. When β = 0.58, the classification accuracy of the algorithm in this paper is above 90%. Using the improved DTA proposed in this paper, a set of mechanical equipment FD system is constructed, and the historical data of the motor is analyzed by the DTA.KeywordsDT AlgorithmMechanical EquipmentFault DiagnosisDiagnostic System
