Knowledge Discovery and Diagnosis Using Temporal-Association-Rule-Mining-Based Approach for Threshing Cylinder Blockage Article Swipe
Yehong Liu
,
Xin Wang
,
Dong Dai
,
Can Tang
,
Xu Mao
,
Du Chen
,
Yawei Zhang
,
Shumao Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture13071299
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture13071299
Accurately diagnosing blockages in a threshing cylinder is crucial for ensuring efficiency and quality in combine harvester operations. However, in terms of blockage diagnostic methods, the current state of affairs is characterized by model-based approaches that can be highly time-consuming and difficult to implement, while data-driven approaches lack interpretability. To address this situation, we propose a temporal association rule mining (TARM)-based fault diagnosis method for identifying threshing cylinder blockages and discovering knowledge. This study performs field trials by varying the actual feed rate and obtains datasets for three blockage classes (slight, moderate, and severe). Firstly, a symbolic aggregate approximation (SAX) method is employed to reduce the data dimensionality and to construct the transaction set with a sliding window. Next, a cSpade method is used to mine and extract strong association rules by applying improved support, confidence, and lift indicators. With the established strong association rules, this study can comprehensively elucidate the variation pattern of each characteristic under several blockage failure conditions and can effectively identify blockage faults. The results demonstrate that the proposed method effectively distinguishes between three levels of blockage faults, achieving an overall diagnostic accuracy of 0.94. And the method yields precisions of 0.90, 0.92, and 0.99 and corresponding recalls of 0.90, 0.93, and 0.98 for slight, medium, and severe levels of blockage faults, respectively. Specifically, the knowledge acquired from the extracted strong association rules can effectively explain the operational characteristics of a combine harvester when its threshing cylinders are blocked. Furthermore, the proposed approach in this study can provide a reasonable and reliable reference for future research on threshing cylinder blockages.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/agriculture13071299
- https://www.mdpi.com/2077-0472/13/7/1299/pdf?version=1687689347
- OA Status
- gold
- Cited By
- 6
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382063046
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382063046Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/agriculture13071299Digital Object Identifier
- Title
-
Knowledge Discovery and Diagnosis Using Temporal-Association-Rule-Mining-Based Approach for Threshing Cylinder BlockageWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-25Full publication date if available
- Authors
-
Yehong Liu, Xin Wang, Dong Dai, Can Tang, Xu Mao, Du Chen, Yawei Zhang, Shumao WangList of authors in order
- Landing page
-
https://doi.org/10.3390/agriculture13071299Publisher landing page
- PDF URL
-
https://www.mdpi.com/2077-0472/13/7/1299/pdf?version=1687689347Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2077-0472/13/7/1299/pdf?version=1687689347Direct OA link when available
- Concepts
-
Threshing, Association rule learning, Interpretability, Data mining, Computer science, Lift (data mining), Artificial intelligence, Pattern recognition (psychology), Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 2Per-year citation counts (last 5 years)
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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