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2 Journal of Sustainable Mining

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Research on vibration-based early diagnostic system for excavator motor bearing using 1-D CNN

Yandagsuren Dorjsuren, Kurauchi Tatsuki, Toriya Hisatoshi, Ikeda Hajime, Adachi Tsuyoshi, Kawamura Youhei

Journal of Sustainable Mining

2023

Vol. 22, iss. 1

65--80

In mining, super-large machines such as rope excavators are used to perform the main mining operations. A rope excavator is equipped with motors that drive mechanisms. Motors are easily damaged as a result of harsh mining conditions. Bearings are important parts in a motor; bearing failure accounts for approximately half of all motor failures. Failure reduces work efficiency and increases maintenance costs. In practice, reactive, preventive, and predictive maintenance are used to minimize failures. Predictive maintenance can prevent failures and is more effective than other maintenance. For effective predictive maintenance, a good diagnosis is required to accurately determine motor-bearing health. In this study, vibration-based diagnosis and a one-dimensional convolutional neural network (1-D CNN) were used to evaluate bearing deterioration levels. The system allows for early diagnosis of bearing failures. Normal and failure-bearing vibrations were measured. Spectral and wavelet analyses were performed to determine the normal and failure vibration features. The measured signals were used to generate new data to represent bearing deterioration in increments of 10%. A reliable diagnosis system was proposed. The proposed system could determine bearing health deterioration at eleven levels with considerable accuracy. Moreover, a new data mixing method was applied.

Application of entropy method for estimating factor weights in mining-method selection for development of novel mining-method selection system

Manjate Pansilvania Andre Manjate, Saadat Mahdi, Toriya Hisatoshi, Inagaki Fumiaki, Kawamura Youhei

Journal of Sustainable Mining

2021

Vol. 20, iss. 4

296--308

Mining-method selection (MMS) is one of the most critical and complex decision making processes in mine planning. Therefore, it has been a subject of several studies for many years culminating with the development of different systems. However, there is still more to be done to improve and/or create more efficient systems and deal with the complexity caused by many influencing factors. This study introduces the application of the entropy method for feature selection, i.e., select the most critical factors in MMS. The entropy method is applied to assess the relative importance of the factors influencing MMS by estimating their objective weights to then select the most critical. Based on the results, ore strength, host-rock strength, thickness, shape, dip, ore uniformity, mining costs, and dilution were identified as the most critical factors. This study adopts the entropy method in the data preparation step (i.e., feature selection) for developing a novel-MMS system that employs recommendation system technologies. The most critical factors will be used as main variables to create the dataset to serve as a basis for developing the model for the novel-MMS system. This study is a key step to optimize the performance of the model.