The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.

Transformers are indispensable in the industry sector and society in general, as they play an important role in power distribution, allowing the delivery of electricity to different loads and locations. Because of their great importance, it is necessary that they have high reliability, so that their failure does not cause additional losses to the companies. Inside a transformer, the primary and secondary turns are insulated by oil. Analyzing oil samples, it is possible to diagnose the health status or type of fault in the transformer. This paper combines Fuzzy Logic and Neural Network techniques, with the main objective of detecting and if possible predicting failures, so that the maintenance technicians can make decisions and take action at the right time. The results showed an accuracy of up to 95% in detecting failures. This study also highlights the importance of predictive maintenance and provides a unique approach to support decision-making for maintenance technicians.

Predictive maintenance is very important in industrial plants to support decisions aiming to maximize maintenance investments and equipment’s availability. This paper presents predictive models based on long short-term memory neural networks, applied to a dataset of sensor readings. The aim is to forecast future equipment statuses based on data from an industrial paper press. The datasets contain data from a three-year period. Data are pre-processed and the neural networks are optimized to minimize prediction errors. The results show that it is possible to predict future behavior up to one month in advance with reasonable confidence. Based on these results, it is possible to anticipate and optimize maintenance decisions, as well as continue research to improve the reliability of the model.

Data is an important resource for gaining knowledge about the behavior and condition monitoring of machines, enabling the estimation of parameters and the prediction of failures. However, in industrial environments, sensor interruptions often create gaps in the time series, which affects the reliability of the data. To overcome this challenge, this paper proposes an imputation strategy based on recurrent neural networks, in particular long short-term memory (LSTM) models, within a multivariate encoder–decoder architecture. This approach utilizes correlations between variables to reconstruct missing values, resulting in more complete and robust datasets. Experimental results with multivariate time series show that the proposed method achieves accurate imputation, with errors as low as RMSE = 2.33 and $R^2$ = 0.90 for some variables. Comparisons with alternative architectures, including GRU and Dense networks, show that LSTM excels in specific cases (e.g., VL3, $R^2$ = 0.45), while the Dense architecture provides more stable performance across most variables. In particular, the Dense model achieved the best overall balance between accuracy and robustness, reaching RMSE = 2.33 and $R^2$ = 0.90 for the best-performing variables, while the LSTM achieved the lowest error values in targeted scenarios, confirming its suitability for capturing complex temporal dependencies. Overall, this study highlights the feasibility of using recurrent neural networks to exploit temporal correlations for reliable data recovery, even under conditions of signal interruption in factory environments.

The maintenance paradigm has evolved over the last few years and companies that want to remain competitive in the market need to provide condition-based maintenance (CBM). The diagnosis and prognosis of the health status of equipment, predictive maintenance (PdM), are fundamental strategies to perform informed maintenance, increasing the company’s profit. This article aims to present a diagnosis and prognosis methodology using a hidden Markov model (HMM) classifier to recognise the equipment status in real time and a deep neural network (DNN), specifically a gated recurrent unit (GRU), to determine this same status in a future of one week. The data collected by the sensors go through several phases, starting by cleaning them. After that, temporal windows are created in order to generate statistical features of the time domain to better understand the equipment’s behaviour. These features go through a normalisation to produce inputs for a feature extraction process, via a principal component analysis (PCA). After the dimensional reduction and obtaining new features with more information, a clustering is performed by the K-means algorithm, in order to group similar data. These clusters enter the HMM classifier as observable states. After training using the Baum–Welch algorithm, the Viterbi algorithm is used to find the best path of hidden states that represent the diagnosis of the equipment, containing three states: state 1—“State of Good Operation”; state 2—“Warning State”; state 3—“Failure State”. Once the equipment diagnosis is complete, the GRU model is used to predict the future, both of the observable states as well as the hidden states coming out from the HMM. Thus, through this network, it is possible to directly obtain the health states 7 days ahead, without the necessity to run the whole methodology from scratch.

Predictive maintenance strategies are becoming increasingly more important with the increased needs for automation and digitalization within pulp and paper manufacturing sector.Hence, this study contributes to examine the most efficient pre-processing approaches for predicting sensory data trends based on Gated Recurrent Unit (GRU) neural networks. To validate the model, the data from two paper pulp presses with several pre-processing methods are utilized for predicting the units’ conditions. The results of validation criteria show that pre-processing data using a LOWESS in combination with the Elimination of discrepant data filter achieves more stable results, the prediction error decreases, and the predicted values are easier to interpret. The model can anticipate future values with MAPE, RMSE and MAE of 1.2, 0.27 and 0.30 respectively. The errors are below the significance level. Moreover, it is identified that the best hyperparameters found for each paper pulp press must be different.