Predictive Maintenance of Heavy Equipment Machines using Neural Network Based on Operational Data

Ahya Radiatul Kamila; Gerry Hudera  Derhass; Johanes Fernandes Andry; Francka Sakti Lee; Very Budiyanto; Velly Anatasia

doi:10.31154/cogito.v11i2.555.229-241

Authors

Ahya Radiatul Kamila Program Studi Sains Data, Universitas Bunda Mulia
Gerry Hudera Derhass Kurnia Prima Nastari Corp
Johanes Fernandes Andry Program Studi Sistem Informasi, Universitas Bunda Mulia
Francka Sakti Lee Program Studi Sistem Informasi, Universitas Bunda Mulia, Jakarta
Very Budiyanto Universitas Bunda Mulia
Velly Anatasia Universitas Bunda Mulia

DOI:

https://doi.org/10.31154/cogito.v11i2.555.229-241

Keywords:

Artificial neural network, Over sampling method, Preventive maintenance, Z-score

Abstract

Preventive maintenance is a routine maintenance strategy that aims to maximize equipment life cycle and prevent unplanned downtime which causes increased repair costs. When carrying out this maintenance, error in selecting machines need to be anticipated to avoid company losses. This research aims to reduce human error in machine selection for preventive maintenance using deep learning. The dataset used in this research is operational data of heavy equipment machine dataset from one of the palm oil companies in Indonesia with 9 independent features and 1 dependent feature. Dependent feature is a target feature contain two target classes representing effective and ineffective machines. The dataset in this study contains outlier, feature scales that are very different, and imbalanced data class. To handle outlier and standardise data scale, the Z-score method is used. Meanwhile, the over sampling method is used to handle imbalanced data classes. To obtain the best model performance, the number of epochs and two types of optimizers (adam&adamax) of neural network are selected. In selecting the number of epochs, experiments were carried out using 100 epochs. This research obtained the linearity relationship between the number of epochs and accuracy with the accuracy values using Adam and Adamax optimizers were 94.82% and 93.11% at the 100th epoch.

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