| dc.description.abstract | The Sri Lankan garment industry has been
garnering attention by bringing the country a huge
income over the past years. The role of wellfunctioning
machinery is a crucial factor in producing
flawless products in this industry. Hence it is a must
for machinery equipment to work regularly thereby
providing the engineering crew a minimum hassle.
Therefore this research paper presents a predictive
maintenance (PdM) based methodology designed with
the aid of a type of deep learning model, a Gated
Recurrent Unit Neural Network (GRU) to predict a
machinery breakdown due to component failures.
Machinery data were used to create data models which
gave the component malfunctioning as a multiclass
classification output. While researching, to handle the
class imbalance problem, Synthetic Minority
Oversampling Technique (SMOTE) mechanism was
also used to obtain a balanced data distribution.
Various combinations of basic deep learning models
and models based on Recurrent Neural networks
(RNN), GRU, and Long Short-Term Memory networks
(LSTM) were used to train the data models, where the
GRU-SMOTE model outperformed the other models
that had an accuracy of 98.77% along with fine scores
for macro average precision, macro average recall,
and macro average f1-score. These early hand
predictions can be therefore utilized to face sudden
machinery failures that will allow the mechanical
crews to plan and schedule maintenance work
efficiently preventing the expenditure of unnecessary
time and resource wastages. | en_US |
