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. 2022 Dec 28;23(1):324.
doi: 10.3390/s23010324.

Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things

Ali M Al Shahrani  1 , Madani Abdu Alomar  2 , Khaled N Alqahtani  3 , Mohammed Salem Basingab  4 , Bhisham Sharma  5 , Ali Rizwan  4
Affiliations

Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things

Ali M Al Shahrani et al. Sensors (Basel). .

Abstract

Industrial automation uses robotics and software to operate equipment and procedures across industries. Many applications integrate IoT, machine learning, and other technologies to provide smart features that improve the user experience. The use of such technology offers businesses and people tremendous assistance in successfully achieving commercial and noncommercial requirements. Organizations are expected to automate industrial processes owing to the significant risk management and inefficiency of conventional processes. Hence, we developed an elaborative stepwise stacked artificial neural network (ESSANN) algorithm to greatly improve automation industries in controlling and monitoring the industrial environment. Initially, an industrial dataset provided by KLEEMANN Greece was used. The collected data were then preprocessed. Principal component analysis (PCA) was used to extract features, and feature selection was based on least absolute shrinkage and selection operator (LASSO). Subsequently, the ESSANN approach is proposed to improve automation industries. The performance of the proposed algorithm was also examined and compared with that of existing algorithms. The key factors compared with existing technologies are delay, network bandwidth, scalability, computation time, packet loss, operational cost, accuracy, precision, recall, and mean absolute error (MAE). Compared to traditional algorithms for industrial automation, our proposed techniques achieved high results, such as a delay of approximately 52%, network bandwidth accomplished at 97%, scalability attained at 96%, computation time acquired at 59 s, packet loss achieved at a minimum level of approximately 53%, an operational cost of approximately 59%, accuracy of 98%, precision of 98.95%, recall of 95.02%, and MAE of 80%. By analyzing the results, it can be seen that the proposed system was effectively implemented.

Keywords: Internet of Things (IoT); elaborative stepwise stacked artificial neural networks (ESSANN) algorithm; industrial automation; industrial environment; least absolute shrinkage and selection operator (LASSO); machine learning; principal component analysis (PCA); robotics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Advantages of industrial automation.
Figure 2
Figure 2
Elaborative stepwise stacked artificial neural networks. Level of input: It receives inputs given by the user in a variety of ways. Level of hidden: Hidden levels are shown between the input and output levels. It performs all the computations necessary to uncover the connections and hidden characteristics. Level of output: This layer is used to communicate the output after the data have undergone several alterations in the hidden layer.
Figure 3
Figure 3
Comparison of delay [23,24,25,26].
Figure 4
Figure 4
Comparison of network bandwidth [23,24,25,26].
Figure 5
Figure 5
Comparison of scalability [23,24,25,26].
Figure 6
Figure 6
Comparison of computation time [23,24,25,26].
Figure 7
Figure 7
Comparison of Packet loss [23,24,25,26].
Figure 8
Figure 8
Comparison of operational cost [23,24,25,26].
Figure 9
Figure 9
Comparison of accuracy, precision, and recall.
Figure 10
Figure 10
Comparison of MAE.
See this image and copyright information in PMC

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