# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 4 Discussion
This is also due to lower complexity. In the case of the convolutional network, the learning time was about 10 s per epoch, which gives us 500 s per session, or more than 8 min. In this case, the RNN performs best, where the transition time was about 13 s. To optimise learning, the data could be fed into the network at every epoch, rather than stored in computer memory. Note that the data were not very complex and that the differences between rest and motion signals are significant [30; 31; 32]. In terms of previous implementations of CNNs with multiple layers, the articles presented here used neural networks with similar structures. The authors used a 3D CNN that classifies collected data that are converted from 1D to 3D images. However, the article focused on the issue of the workload degree. Each feature is extracted in 3D layers, where they are later augmented by a weighting factor. A short-term memory algorithm was also used in the study to predict the focus time based on task difficulty. The author of the study achieved 90.8% accuracy with his own set of signals, and 93.9% on a public base. To investigate human performance under load, they performed a Sternberg task, i.e., memorising combined letter and number stimuli during an encoding phase in which the stimuli consisted of combinations of the alphabet (A to Z) and a number (0 to 9). The network architecture used, among other things, max-pooling operations between the separate layers of the spliced network [33]. For the RNN-LTSM, the papers published so far described a way to classify people suffering from epilepsy, targeting the diagnosis of general as well as focal epilepsy. In the proposed network architecture, it was possible to distinguish a recurrent network as well as a layer related to short-term memory, RNN-LTSM. The proposed algorithm achieved an accuracy of 96.7% [34]. For the RNN (GRU) network, the authors, in order to improve the model they presented, developed an algorithm to randomly combine the training data to improve its classification accuracy. In the paper, they achieved an accuracy of 82.92%. The authors of this paper focused on eye movement data using different eye stimulation techniques. A variant of the GRU recurrent network based on a 3-layer complex network was used [35]. A deep learning neural network approach was also presented, where results were achieved at 90%. The neural networks presented accepted multi-column data as input. The authors cited the long learning process of 1.6 h as one of the disadvantages. A comparison of the LSTM and GRU algorithms to correctly classify the EEG signal during eyebrow-raising, biting, or blinking movements and the resting state in classifying the corresponding set of signals showed that the LSTM algorithm proved superior in terms of classifying the real-world data sets prepared by the researchers [36]. The semi-supervised fusion of multi-sensor information adapted to an embedded low-rank tensor graph under very low labelling rate conditions has also been explored. This approach focuses on semi-supervised learning for intelligent diagnostics using data from multiple sensors when only a small amount of labelled data are available. The method combines signals from multiple sensors into a unified model, using a very small number of labelled signals to develop a robust learning system. Low-order tensor learning, constrained by the kernel norm of the tensor, helps to capture relevant features from the combined sensor data. In addition, regularisation of the manifold is used to infer potential label information from a large amount of unlabelled data. The combined techniques aim to improve diagnostic accuracy under extremely limited labelling conditions, improving predictive maintenance. Overall, the approach maximises the utility of unlabelled data through semi-supervised learning [37]. An intelligent method for diagnosing aero engine bearing faults with high accuracy with limited samples addresses the challenge of this diagnosis when only limited data samples are available.