An updated methodology to analyse the idling of agricultural tractors

Molari, Giovanni; Lenzini, Nicola; Fiorati, Stefano

doi:10.1016/j.biosystemseng.2019.09.001

Cited by 27 publications

(19 citation statements)

References 17 publications

(20 reference statements)

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“…In the J1939 application layer protocol, the parameter group is defined in detail, including the parameter update rate, effective data length, data page, PDU format, PDU details, default priority, and the content of the parameter group, and each parameter group PG has corresponding parameter group number (PGN) [17]. At the same time, each parameter of the J1939 protocol is assigned a number (SPN), which defines the physical meaning of the bytes in the PDU data field, including data length, data type, resolution, range, and reference label [18,19]. Details are shown in Table 1.…”

Section: Experimental Data Collection Based On Can Busmentioning

confidence: 99%

See 1 more Smart Citation

Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm

Xiao

Wang

et al. 2020

Shock and Vibration

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With the rapid development of high-power tractor, the fault diagnosis of high-power tractor has become more and more important for ensuring the operating safety and efficiency. PSO is an iterative optimization evolutionary algorithm, which can iterate through different particles to find the optimal solution. However, there is only one population in the standard PSO algorithm, and the information exchange between the populations is relatively single, which can easily lead to the stagnation of the development of the population. In this paper, due to high-power tractor diesel engine fault complexity, fault correlation, and multifault concurrency, a multigroup coevolution particle swarm optimization BP neural network for diesel engine fault diagnosis method was proposed. First, the USB-CAN device was used to collect data of 8 items of the diesel engine under five different working conditions, and the data was parsed through the SAE J1939 protocol; then, the BP neural network was reconstructed, and a competitive multiswarm cooperative particle swarm optimizer algorithm (COM-MCPSO) was used to optimize its structure and weights. Finally, the data of optimized neural network under five different fault conditions show that, compared with BP neural network and PSO optimized BP neural network, the fault diagnosis of COM-MCPSO optimized BP neural network not only improves the network training speed, but also enhances generalization ability and improves recognition accuracy.

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Section: Experimental Data Collection Based On Can Busmentioning

confidence: 99%

“…From the above parameters, the actual physical value corresponding to each parameter can be calculated. According to the original hexadecimal number of J1939 protocol parameter group PG, the corresponding design physical quantity can be obtained from formula (19) [20,21].…”

Section: Experimental Data Collection Based On Can Busmentioning

confidence: 99%

Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm

Xiao

Wang

et al. 2020

Shock and Vibration

View full text Add to dashboard Cite

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“…Up to 5% reduction in fuel consumption can be obtained by using an improved ICE control, with a benefit also on the total emissions [9]. Moreover, due to tractors usual working cycles, idling condition has a relevant contribution in terms of environmental impact and engine life [10], [11]. So, idling reduction strategies and idling stop devices are very important in this application [12].…”

Section: Introductionmentioning

confidence: 99%

Electrification of Agricultural Machinery: A Review

et al. 2021

Self Cite

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Today, agriculture industry has a significant impact in global greenhouse gas emissions. A large amount of pollutants come from diesel internal combustion engines, widely used in agricultural machinery. Since mechanization in agriculture is fundamental to achieve a proper food production for a growing human population, changes are needed in common agriculture engineering thinking in order to develop new farming machinery that could outperform conventional ones in terms of environmental impact, as well as performance, productivity and safety. Electrification is a feasible solution. A comprehensive review about agricultural machinery electrification is reported in this paper, with a particular focus on hybrid electric tractors and their implements. The introduction of electric drives in farming tractors is discussed in detail by looking at the main findings in literature and considering state-of-the-art technology. Proposals and prototypes from manufacturers are covered too, as well as economic assessments and future trends.

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“…According to the Federal State Statistics Service of the Russian Federation and [1][2][3][4][5], over the past twenty years in the agriculture of Russia there has been a significant reduction in the number of agricultural machines, including the tractors (figure 1). This trend is fully valid only for agricultural enterprises, excluding peasant farms and micro-enterprises.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the reasons for the failures of the tractor microclimate system

et al. 2021

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In the process of performing technological operations, any agricultural machinery is exposed to a large number of changing factors. The result of performing technological operations, i.e. the assessment of performance indicators, in most cases, is performed by the organoleptic method, which is characterized by a significant error (up to 40%) and labour intensity. The use of automatic means of control gives an undoubted advantage in convenience of use and in speed of obtaining results. Given the complexity and labour intensity of assessing the quality indicators of agricultural machinery, even partial automation of this process in practice gives a significant (tens of percent) economic effect and becomes the basis for the formation of digital agriculture. We have analyzed the existing methods for controlling the quality indicators of technological operations using the example of the grain harvesting process. In particular, the possibilities of improving the methods of monitoring the quality indicators of the work of the combine harvester are considered.

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An updated methodology to analyse the idling of agricultural tractors

Cited by 27 publications

References 17 publications

Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm

Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm

Electrification of Agricultural Machinery: A Review

Investigation of the reasons for the failures of the tractor microclimate system

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