| dc.description.abstract | CONTEXT: Industry 4.0 (I4.0) provides connectivity, data volume, new devices, miniaturization, inventory reduction, personalization, and controlled production. In this new era, production customization and data availability are essential to generate information that allows decision-making. The possibility of predicting the need for maintenance in the future and using this information for other processes is one of the manufacturing process challenges. In this context, this thesis proposal transcends the specific fact of applying predictive maintenance (PdM) and suggests ways to integrate processes, focusing on maintenance and
production schedules. OBJECTIVE: The objective is to create the Predictive Maintenance & Schedule (PdMS) to integrate maintenance and production schedules in a predictive way. At each sensor data reading and operational information, the machine’s remaining useful life (RUL) is predicted, deciding whether the machine will be part of the production process or not. Reinforcing that, this new Industry scenario allows Computing Applications, together with artificial intelligence and distributed computing, to become more effective in manufacturing processes. With the PdMS creation, the idea is to reduce downtime, improve communication between the maintenance and production sectors and allow future integration with the production, storage, and logistics sectors. METHODOLOGY: The PdMS creation process was divided into two phases: (i) related to PdM, which describes to create and combine degradation indices using similarity patterns and application Savitzky-Golay and Kalman smoothing filters that allow noisy data to identify time-based failures; (ii) related to the scheduling problem and the integration with the results generated by the PdM, which describes the schedule generation, maintenance verification and graphics generation to control and follow up the production schedule. To evaluate the PdMS, a sample predictive maintenance dataset provided by Microsoft was used. We searched for data with characteristics that could contribute to the idea of defining an approach that encourages the adoption of predictive maintenance in factories that already have telemetry in their assets but still perform corrective or preventive maintenance. RESULTS: To evaluate the results, we compared several models based on Deep Neural Networks (DNN) and Recurrent Neural Networks (RNN). Regression Random Forest (RRF) was used to contribute to feature selection and was performed a comparison between Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Networks, and Deep Feed Forward (DFF) network. The results were visually evaluated and by criteria based on errors: Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error (MSE), Determination Coefficient R2 and Mean Absolute Percentage Error (MAPE). The best results presentes RMSE = 8:789;MSE = 77:253;MAE = 2:262;R2 = 0:848;MAPE = 92:22. CONCLUSION: As a contribution, this work brings a systematic review with a taxonomy proposal, challenges identification, and open questions regarding I4.0 with a focus on PdM. The PdMS model was created from the challenges presented, which presented the decisions, strategies, and architecture that resulted in the prediction of failures in noisy data with five-day anticipation in the data set used for the experiment, thus enabling the intended outcome integration simulation. | en |
