The concept of "Shipyard 4.0" [55] is described as the result of the application of the Industry 4.0 to this sector. Therefore, the Shipyard 4.0 initiative must be the response of the shipbuilding sector to the digital transformation.
Social network analysis (SNA)
In the shipbuilding industry, both technologies are already being used in small applications for training and positioning of parts. In the shipbuilding industry, there are already several applications in terms of ship design for overall performance optimization [94].
Results
First, a non-symmetric matrix is created in which non-linear dependencies between KETs are displayed. For example, VAR is dependent on M&S but not the other way around.
Conclusions
The mediating effect of employee engagement on the relationship between Industry 4.0 and operational performance improvement. Scenarios of development of Industry 4.0 in the conditions of knowledge economy's formation and their consequences for modern economic systems.
TOP 1%
OPC UA
It is the next generation of the original OPC which is applied in various technologies such as building automation or process control. To make this possible, the plant's EMS and communication technologies must be studied and adapted. The high-level structure and requirements of the EMS are explained along with more common communication technologies and protocols.
Its advantages and disadvantages are presented and important factors for the selection of sensor technologies are described. Factory Energy Infrastructure as a Virtual Power Plant: Smart Energy Management DOI: http://dx.doi.org/10.5772/intechopen.88861. Distributed under the terms of the Creative Commons Attribution - NonCommercial 4.0 license (https://creativecommons.org/ .licens/by-nc/4.0/), which permits use, distribution, and reproduction for noncommercial purposes, provided the original is cited properly.
Optimal planning and operation of the ESS for a prosumer market environment in a grid-connected industrial complex. With the current industry demands and the increasing complexity of the systems, it is essential to integrate CBM methodologies that are able to cope with the variability and complexity of manufacturing processes. This chapter discusses some of the deep learning techniques that promise major advances in intelligent fault diagnosis in industrial electromechanical systems.
Introduction
Industry 4.0 is a recent automation trend and rotary machines play a very important role when it comes to meeting the demands and challenges of smart manufacturing. In recent years, various deep learning techniques have been successfully applied in various fields of research, such as image recognition, robotics, and abnormality detection in clinical studies. DL as a branch of machine learning derives from the learning capacity of artificial neural networks (ANN); however, the learning capacity of ANNs is limited and presents difficulties in adjusting the weights with error correction (backpropagation).
Therefore, various DL architectures have been developed based on stacking multiple layers of ANNs, such as autoencoders, convolutional neural networks, or constrained Boltzmann machines. The main reason for the application of DL-based techniques in the study of the state of electromechanical systems is due to the limitation presented by the basic analysis schemes. A traditional diagnostic scheme consists of feature extraction and engineering selection from acquisition data, followed by applying a dimensionality reduction process and training a machine learning-based prediction model involving support vector machines (SVMs). ), simple neural networks. (NN), or regression algorithms.
Unlike traditional schemes based on machine learning, DL schemes are not limited to characterizing systems with only a set of pre-established features, but by constructing structures based on neural networks, they are able to hierarchical representations to extract from the data. These representations or extracted features have a greater representational capacity because the schemes for their extraction are by non-linear algorithms; with this, a structure based on deep learning is able to learn the adjacent non-linearities of faults and multiple operating conditions of modern manufacturing processes that integrate rotating systems among their components. After the brief summary of the DL tools, the main applications of deep learning concern the monitoring of the state of electromechanical systems.
Deep neural networks
- Auto encoders
- Restricted Boltzmann machine
One of the main DNN-based feature extraction architectures are convolutional neural networks (CNNs). A convolutional neural network is a kind of artificial neural network specially designed for identifying patterns of the data [3]. The output of a CNN layer is a 3D tensor, which consists of a stack of arrays called feature maps; these functions can be used as inputs for a next layer of the CNN scheme.
Convolution puts the input signal through a set of convolutional operators or filters, each of which activates certain features from the data. An autoencoder is a type of symmetric neural network that tries to learn features in a semi-supervised manner while minimizing reconstruction error. Also, am can be thought of as a representation of the features that the encoder processes learning from the input data.
To improve the performance of the traditional auto-encoder, a sparse constraint term is introduced, generating a variant known as sparse auto-encoder (SAE) [4-6]. During the positive step, the network parameters are changed to replicate the training set, while during the negative step, it tries to recreate the data based on the current network configuration. Constrained Boltzmann machines can be used in deep learning networks to extract characteristic patterns from the data.
Applications of deep learning in condition-based monitoring For several years, the best tools for monitoring electromechanical systems
- Feature extraction
- Dimensionality reduction
- Novelty detection
By using deep learning-based schemes, it was possible to reduce the dependency on feature design and limit manual feature selection; in this way, it is possible to get rid of human experience or a great deal of prior knowledge about the problem. In this approach, an image is used as input for a CNN to learn complex features of the system. As we saw above, DNN-based structures can learn intrinsic data relationships; however, a reduced representation of the data can be created during this learning process.
Therefore, a dimensionality reduction based on the automatic encoder can provide a better display that helps to distinguish between machine states. An example of the difference between applying a linear reduction technique. Novelty detection is the method used to recognize test data that differs in some aspects from the data available during training [17].
However, most assume that the data must be under the same operating conditions and that the data distributions for each class considered are the same. One of the research related to transfer learning applied to fault diagnosis in industrial systems is presented in [20]. The results obtained show considerable efficiency; however, the proposed scheme still considers that the samples of the source and target domains are the same.
Experimental case of deep learning in CBM
New trends in the use of artificial intelligence for industry 4.0. target task; however, it preserves part of the weights with which the homework network was trained. However, this performance is affected when the differences between the source task and the target task are increased. Incorporating schemes based on transfer learning allows us to adapt different structures based on DL to transfer the experience learned in a diagnostic task and improve performance in a similar but different task.
To verify the effectiveness of a non-linear dimensionality reduction technique, the projections resulting from the reduction process of the three techniques are shown in Figure 8. Finally, the classification stage with the NN-based classifier is configured with five neurons in the layer hidden, except a sigmoid logistic function is used as the output activation function and 100 epochs are considered for training using the back propagation rule. The classification ratios for the test sets are approximately 95% for PCA, 98% for LDA, and 99% for the autoencoder.
Two important things can be concluded from this study: first, they highlight the capabilities of the SAE-based approach to automatically learn the most important features (those that provide more discriminative information) and that this translates into increased performance. Second, in terms of dimensionality reduction, the autoencoder-based approach shows better discriminative abilities during visualization of the results than the linear PCA and LDA methods, thereby facilitating the classification task.
Conclusion and future challenges
Rotating machine-based electromechanical system used to demonstrate the practical implementation of the proposed method. Indeed, the use of Matlab facilitates the signal processing for performing the condition assessment of the electromechanical system. SOM1 model obtained and this SOM model only characterizes the healthy state of the electromechanical system.
Figure 4 shows a visual representation of the novelty detection achieved with the first modeled SOM1grid neuron. Novelty detection performed by SOM1 during the evaluation of the first tested failure condition in the electromechanical system, 25% uniform wear in the gearbox. Novelty detection is performed by SOM2 during the assessment of the second defective condition, 50% uniform wear in the gearbox.
In Figure 6, a visual representation of the performed novelty detection is shown during the evaluation of the SOM3 model. Novelty detection performed with SOM3 obtained to estimate the third failure state, 75% uniform wear in the gearbox. Over time, a continuous and gradual drift occurs in the sensor observations, resulting in incorrect measurements.
Thus, the data set is the time series evolution of key process parameters. Depending on the granularity of the data (continuous, cyclic, or batch), different preprocessing techniques can be applied to increase the performance of the subsequent machine learning classifier.
