Measurement and diagnostic system for detecting and classifying faults in the rotary hay tedder using multilayer perceptron neural networks

• Published in: Engineering applications of artificial intelligence Vol. 133; p. 108513
• Main Authors: Mystkowski, Arkadiusz, Wolniakowski, Adam, Idzkowski, Adam, Ciężkowski, Maciej, Ostaszewski, Michał, Kociszewski, Rafał, Kotowski, Adam, Kulesza, Zbigniew, Dobrzański, Sławomir, Miastkowski, Krzysztof
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2024
• Subjects: Accelerometers; Accuracy; Agriculture; ANOVA; Deep learning; Feature vector; Machine fault detection; Machine learning; Multi-layer perceptron neural network; Rotary hay tedder; Vibration sensors; Accelerometers; Machine fault detection; Deep learning; Multi-layer perceptron neural network; Accuracy; Vibration sensors; Rotary hay tedder; Feature vector; ANOVA; Machine learning; Agriculture
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2024.108513

The implementation of preventive maintenance measures, which aim to assure the optimal performance of rotary machines and minimize the occurrence of substantial damage, has gained significant importance in the agricultural machinery industry. As part of this work, several measurements and computations were conducted to develop effective methods for detecting and classifying machine damages based on raw measurement data from accelerometers acquired both in the laboratory and real-life conditions. For this purpose a rotary hay tedder was equipped with a self-developed measuring and diagnostic system. The hardware and network communication components of this system were described. Machine learning methods were applied and the multi-layer perceptron (MLP) neural-network based models were created. The diagnostic models were made using different machine learning algorithms with 90 different configurations of neural-networks in total, including 30 different hidden layer arrangements and three activation functions (relu, softmax and sigmoid). Several learning scenarios were considered in order to determine and to distinguish between a healthy state and a machine failure, such as a missing or broken tedder tine. Based on laboratory experiments and meadow tests, 20 best metrics were selected for the input feature vector. When conducting laboratory and on-site tests, the best efficiency was achieved for the multi-layer perceptron neural-network architecture with two hidden layers (size of 20 and 20) and the relu activation function, and the Adam optimizer. Accuracy, as the ratio of correct predictions to total predictions made, for different diagnostic models was estimated. The confusion matrices were given to present the results for 10 rotors. Data efficiency for different diagnostic models and hyperparameters was also presented. For tests on the meadow, the average accuracy was 79% for detecting a missing tine, and 69% for detecting a tine failure (a missing or broken tine). In the case of the laboratory-based experimental results, the average accuracy for detecting a broken tine in the rotor was 92.5%. Therefore, based on the results, the proposed approach can be used for rotary hay tedder fault diagnosis. •The efficiencies of various architectures of the MLP neural network were given after selecting the 20 best features from 60.•With the new system, it was possible to detect a machine malfunction: a missing or broken tine, or an unknown tine fault.•When training and testing were done using data obtained on-site, the average accuracy for detecting a missing tine was 79%.