Knowledge Discovery and Diagnosis Using Temporal-Association-Rule-Mining-Based Approach for Threshing Cylinder Blockage

Yehong Liu, Xin Wang (), Dong Dai, Can Tang, Xu Mao, Du Chen, Yawei Zhang and Shumao Wang
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Yehong Liu: College of Engineering, China Agricultural University, Beijing 100083, China
Xin Wang: College of Engineering, China Agricultural University, Beijing 100083, China
Dong Dai: College of Engineering, China Agricultural University, Beijing 100083, China
Can Tang: College of Engineering, China Agricultural University, Beijing 100083, China
Xu Mao: College of Engineering, China Agricultural University, Beijing 100083, China
Du Chen: College of Engineering, China Agricultural University, Beijing 100083, China
Yawei Zhang: College of Engineering, China Agricultural University, Beijing 100083, China
Shumao Wang: College of Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2023, vol. 13, issue 7, 1-21

Abstract: 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.

Keywords: fault diagnosis; temporal association rule mining; knowledge discovery; threshing cylinder blockage; combine harvester (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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