Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Choe, Hyeon O.; Lee, Meong-Hun

doi:10.3390/agriculture13112124

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Extensive research has been dedicated to fault diagnosis within the agricultural domain. Choe et al [10] devised and implemented a system leveraging recurrent neural network algorithms to detect and predict abnormal data. They incorporated ontology technology for fault diagnosis, aiming to prevent potential farm damage caused by errors, malfunctions, and aging-related downtime.…”

Section: Introductionmentioning

confidence: 99%

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

Xie,

Li,

Liu

et al. 2024

Agriculture

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In the context of addressing the challenge posed by limited fault samples in agricultural machinery rolling bearings, especially when early fault characteristics are subtle, this study introduces a novel approach. The proposed multi-domain fault diagnosis method, anchored in data augmentation, aims to discern early faults in agricultural machinery rolling bearings, particularly within an imbalanced sample framework. The methodology involves determining early fault signals throughout the life cycle, constructing early fault datasets with varying imbalance rates for different fault types, and subsequently employing the Synthetic Minority Oversampling Technique (SMOTE) to balance the fault data. The study then extracts relative wavelet packet energy and time-domain sensitive features (variance, peak to peak) from the original and generated fault data to form a multi-domain fault feature vector. This vector is utilized for fault state recognition using a Support Vector Machine (SVM). Evaluation metrics such as accuracy, recall, and F1 values assess the recognition effectiveness for each rolling bearing state, with the overall model recognition evaluated based on accuracy. The proposed method is rigorously analyzed and validated using the XJTU-SY rolling bearing accelerated life test dataset. Comparative analysis is conducted with non-data enhanced fault feature vectors, specifically the relative energy of the wavelet packet, both with and without time-domain features. Experimental results underscore the superior performance of multi-domain fault features in providing a comprehensive description of signal information, leading to enhanced classification performance. Furthermore, the study demonstrates improved classification accuracy and recall rates for the balanced dataset compared to the imbalanced dataset. This research significantly contributes to an effective identification method for the early fault diagnosis of small sample rolling bearings in agricultural machinery.

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Section: Introductionmentioning

confidence: 99%

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

Xie,

Li,

Liu

et al. 2024

Agriculture

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“…Smart farm is a system that utilizes information and communication technology in the agricultural field to improve productivity and stably manage the quality and quantity of crops, and is being actively introduced to indoor environments such as green houses [1][2][3][4]. In the green house, sensor data such as temperature, humidity, CO2, soil moisture, and pH can be easily collected through wired or short-distance wireless communication to monitor crop growth.…”

Section: Introductionmentioning

confidence: 99%

Development of Self-generated and LPWA-based Crop Growth Environment Monitoring and Bigdata Analysis System

2023

Preprint

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Smart farm is a technology that greatly helps improve the productivity and quality of crops and is being actively introduced into indoor environments such as green houses. However, it has not yet been distributed to field-grown crops. The main reason is that, unlike indoor environments, it is very difficult to collect sensor data for monitoring the growth environment of crops in open fields where weather conditions change significantly and power supply is difficult. Additionally, because various sensors are used, the data formats of the devices are different, making it difficult to process. This paper presents a field crop growth environment monitoring and big data analysis system. The proposed system first solves power supply and data communication problems using solar power generation and LPWA technology. Additionally, based on the oneM2M architecture, data from various sensors is transmitted to the server using standardized technology. The transmitted data is stored and managed on the server as big data, and can be used to predict the production and quality of field-grown crops and take appropriate measures. The proposed system is expected to create an environment optimized for the growth of field crops and help prevent and manage diseases.

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Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Cited by 2 publications

References 17 publications

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

Development of Self-generated and LPWA-based Crop Growth Environment Monitoring and Bigdata Analysis System

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