The Use of Deep Learning Methods in Diagnosing Rotating Machines Operating in Variable Conditions

Paweł Pawlik, Konrad Kania and Bartosz Przysucha
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Paweł Pawlik: Department of Mechanics and Vibroacoustics, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland
Konrad Kania: Department of Quantitative Methods in Management, Faculty of Management, Lublin University of Technology, ul. Nadbystrzycka 38 D, 20-618 Lublin, Poland
Bartosz Przysucha: Department of Quantitative Methods in Management, Faculty of Management, Lublin University of Technology, ul. Nadbystrzycka 38 D, 20-618 Lublin, Poland

Energies, 2021, vol. 14, issue 14, 1-17

Abstract: This paper presents the use of artificial neural networks in diagnosing the technical condition of drive systems operating under variable conditions. The effects of temperature and load variations on the values of diagnostic parameters were considered. An experiment was conducted on a testing rig where a variable load was introduced corresponding to the load of the main gearbox of the bucket wheel excavator. The signals of vibration acceleration on the gearbox body, rotational speed, and current consumption of the drive motor for different values of oil temperature were measured. Synchronous analysis was performed, and the values of order amplitudes and the corresponding values of current, speed, and temperature were determined. Such datasets were the learning vectors for a set of artificial deep learning neural networks. A new approach proposed in this paper is to train the network using a learning set consisting only of data from the efficient system. The responses of the trained neural networks to new data from the undamaged system were performed against the response to data recorded for three damage states: misalignment, unbalance, and simultaneous misalignment and unbalance. As a result, a diagnostic parameter as a normalized measure of the deviation of the network results was developed for the faulted system from the result for the undamaged condition.

Keywords: condition monitoring; vibroacoustic diagnostics; gearbox; power transmission systems; neural networks; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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