Fault diagnostic systems for agricultural machinery

Craessaerts, Geert; Baerdemaeker, Josse De; Saeys, Wouter

doi:10.1016/j.biosystemseng.2009.12.004

Cited by 31 publications

(12 citation statements)

References 24 publications

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“…Pseudonymized data is grouped according to certain criteria, which are defined in the descriptions of personal data characteristics, from which the pseudonymized data is directly derived [4,5].…”

Section: Fig 2types Of Personal Datamentioning

confidence: 99%

Theoretical aspects of the protection of personal data of employees of the enterprise by the method of pseudonymization

Gazizov

Gazizova

2020

E3S Web Conf.

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The topic of pseudonymization of personal data has shown, that theoretical and methodological basics in sphere of automatized systems have just started to gain general trend. The majority of studies in this sphere are, commonly, about personal data in general, rarely touching the topic of pseudonymization and depersonalization. Therefore, the topic of pseudonymization has not fully assimilated in enterprise systems and has not grown any popularity, because enterprises tend to choose reliable tools and methods of information security while depersonalization is only beginning its way and is not common for big corporations. This leads to disinterestedness in solving known issues and goals of pseudonymization, universal methods have not been researched. However, low cost and simplicity of this method of personal data protection is turning our attention on it and ask ourselves a question: “Should we have a deep dive in it?”. Answer is obvious – yes. Certainly, this method has its disadvantages and it is not an ideal solution. But it certainly should be distributed worldwide.

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Section: Fig 2types Of Personal Datamentioning

confidence: 99%

Theoretical aspects of the protection of personal data of employees of the enterprise by the method of pseudonymization

Gazizov

Gazizova

2020

E3S Web Conf.

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“…It can be concluded that for the analysis of PF, not only the readings obtained from the metering devices of any parameters are subject to evaluation, but also parameters that cannot be measured must also be taken into account. [3] Studies related to the behavior of technical objects in the conditions of the BS have shown that expert solutions with assessments of the situation require an analysis of the complex of tasks based on the following information:  about the place, type and reason of the PF;  about the mode of functioning at which the PF was manifested;  on the degree of exposure to external factors;  about individual features and characteristics of the object of analysis.…”

Section: Analysis Of the Issue Of Partial Failurementioning

confidence: 99%

About some possibilities of diagnosing partial failures arising in complex agricultural machinery

2020

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The article is devoted to the issue of partial failure in complex technical systems. The authors analyze the diagnostic procedure in agricultural machinery for various purposes, the results of which provide a diagram of a multi-level organization for assessing the condition of equipment. It is also proposed to expand the range of estimated diagnostic parameters for making a more reliable managerial decision. The introduction of expert systems and other similar intelligent systems (for example, expert decision support systems) for diagnosing complex malfunctions is justified.

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“…Building a data-driven predictive model will be a new way of solving traditional problems. During parameter tuning, a large number of noises and vibrations were detected in the combine harvester's working situation [14]- [17]. They interfere with the feature extraction process, resulting in the final extracted key feature parameters showing nonlinearity, no marking, and mutual interference status.…”

Section: Introductionmentioning

confidence: 99%

Combine Harvester Cooling Water Temperature Prediction Based on CDAE-LSTM Hybrid Model

et al. 2021

ELEKTRON ELEKTROTECH

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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.

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Fault diagnostic systems for agricultural machinery

Cited by 31 publications

References 24 publications

Theoretical aspects of the protection of personal data of employees of the enterprise by the method of pseudonymization

Theoretical aspects of the protection of personal data of employees of the enterprise by the method of pseudonymization

About some possibilities of diagnosing partial failures arising in complex agricultural machinery

Combine Harvester Cooling Water Temperature Prediction Based on CDAE-LSTM Hybrid Model

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