Only with new sensor concepts in a network, which go far beyond what the current state-of-the-art can offer, can current and future requirements for flexibility, safety, and security be met. The combination of data from many sensors allows a richer representation of the observed phenomenon, e.g., system degradation, which can facilitate analysis and decision-making processes. This work addresses the topic of predictive maintenance by exploiting sensor data fusion and artificial intelligence-based analysis. With a dataset such as vibration and sound from sensors, we focus on studying paradigms that orchestrate the most optimal combination of sensors with deep learning sensor fusion algorithms to enable predictive maintenance. In our experimental setup, we used raw data obtained from two sensors, a microphone, and an accelerometer installed on a brushless direct current (BLDC) motor. The data from each sensor were processed individually and, in a second step, merged to create a solid base for analysis. To diagnose BLDC motor faults, this work proposes to use data-level sensor fusion with deep learning methods such as deep convolutional neural networks (DCNNs) for their ability to automatically extract relevant information from the input data, the long short-term memory method (LSTM), and convolutional long short-term memory (CNN-LSTM), a combination of the two previous methods. The results show that in our setup, sound signals outperform vibrations when used individually for training. However, without any feature selection/extraction step, the accuracy of the models improves with data fusion and reaches 98.8%, 93.5%, and 73.6% for the DCNN, CNN-LSTM, and LSTM methods, respectively, 98.8% being a performance that, according to our reading, has never been reached in the analysis of the faults of a BLDC motor without first going through the extraction of the characteristics and their fusion by traditional methods. These results show that it is possible to work with raw data from multiple sensors and achieve good results using deep learning methods without spending time and resources on selecting appropriate features to extract and methods to use for feature extraction and data fusion.

Predictive maintenance of equipment requires a set of data collected through sensors, from which models will learn behaviors that will allow the automatic detection or prediction of these behaviors. The objective is to anticipate unexpected situations such as sudden equipment stoppages. Industries are noisy environments due to production lines that involve a series of components. As a result, the data will always be obstructed by noise. Noise-robust predictive maintenance models, which include ensemble and deep learning models with and without data fusion, are proposed to enhance the monitoring of industrial equipment. The work reported in this paper is based on two components, a milling tool, and a motor, with sound, vibration, and ultrasound data collected in real experiments. Four main tasks were performed, namely the construction of the datasets, the training of the monitoring models without adding artificial noise to the data, the evaluation of the robustness of the previously trained models by injecting several levels of noise into the test data, and the optimization of the models by a proposed noisy training approach. The results show that the models maintain their performances at over 95% accuracy despite adding noise in the test phase. These performances decrease by only 2% at a considerable noise level of 15dB signal-to-noise ratio (SNR). The noisy training method proved to be an optimal solution for improving the noise robustness and accuracy of convolutional deep learning models, whose performance regression of 2% went from a noise level of 28dB to 15dB like the other models.

In dynamic industrial environments, strategic sensor placement is key to accurately monitoring equipment and detecting critical events. Despite progress in Industry 4.0 and the Internet of Things, research on optimal sensor placement remains limited. This study addresses this gap by analyzing how sensor placement impacts event detection, using chemical detection as a case study with an open dataset. Detecting gases is challenging due to their dispersion. Effective algorithms and well-planned sensor locations are required for reliable results. Using deep convolutional neural networks (DCNNs) and decision tree (DT) methods, we implemented and tested detection models on a public dataset of chemical substances collected at five locations. In addition, we also implemented a multi-objective optimization approach based on the non-dominated sorting genetic algorithm II (NSGA-II) to identify optimal sensor configurations that balance high detection accuracy with cost efficiency in sensor deployment. Using the refined sensor placement, the DCNN model achieved 100% accuracy using only 30% of the available sensors.