The new evolution of the production and industrial process called Industry 4.0, and its related technologies such as the Internet of Things, big data analytics, and cyber–physical systems, among others, still have an unknown potential impact on sustainability and the environment. In this paper, we conduct a literature-based analysis to discuss the sustainability impact and challenges of Industry 4.0 from four different scenarios: deployment, operation and technologies, integration and compliance with the sustainable development goals, and long-run scenarios. From these scenarios, our analysis resulted in positive or negative impacts related to the basic production inputs and outputs flows: raw material, energy and information consumption and product and waste disposal. As the main results, we identified both positive and negative expected impacts, with some predominance of positives that can be considered positive secondary effects derived from Industry 4.0 activities. However, only through integrating Industry 4.0 with the sustainable development goals in an eco-innovation platform, can it really ensure environmental performance. It is expected that this work can contribute to helping stakeholders, practitioners and governments to advance solutions to deal with the outcomes emerging through the massive adoption of those technologies, as well as supporting the expected positive impacts through policies and financial initiatives.

https://www.mdpi.com/2071-1050/10/10/3740/pdf

Industry 4.0 and Sustainability Implications: A Scenario-Based Analysis of the Impacts and Challenges

Predictive maintenance in the Industry 4.0: A systematic literature review

Shipbuilding companies are upgrading their inner workings in order to create Shipyards 4.0, where the principles of Industry 4.0 are paving the way to further digitalized and optimized processes in an integrated network. Among the different Industry 4.0 technologies, this paper focuses on augmented reality, whose application in the industrial field has led to the concept of industrial augmented reality (IAR). This paper first describes the basics of IAR and then carries out a thorough analysis of the latest IAR systems for industrial and shipbuilding applications. Then, in order to build a practical IAR system for shipyard workers, the main hardware and software solutions are compared. Finally, as a conclusion after reviewing all the aspects related to IAR for shipbuilding, it proposed an IAR system architecture that combines cloudlets and fog computing, which reduce latency response and accelerate rendering tasks while offloading compute intensive tasks from the cloud.

A Review on Industrial Augmented Reality Systems for the Industry 4.0 Shipyard

Industry 4.0 (I4.0) encompasses a plethora of digital technologies effecting on manufacturing enterprises. Most research on this topic examines the effects in the smart factory domain, focusing on ...

The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review

Smart grid communication and information technologies in the perspective of Industry 4.0: Opportunities and challenges

Industry 4.0 can make a factory smart by applying intelligent information processing approaches, communication systems, future-oriented techniques, and more. However, the high complexity, automation, and flexibility of an intelligent factory bring new challenges to reliability and safety. Industrial big data generated by multisource sensors, intercommunication within the system and external-related information, and so on, might provide new solutions for predictive maintenance to improve system reliability. This paper puts forth attributes of industrial big data processing and actively explores industrial big data processing-based predictive maintenance. A novel framework is proposed for structuring multisource heterogeneous information, characterizing structured data with consideration of the spatiotemporal property, and modeling invisible factors, which would make the production process transparent and eventually implement predictive maintenance on facilities and energy saving in the industry 4.0 era. The effectiveness of the proposed scheme was verified by analyzing multisource heterogeneous industrial data for the remaining life prediction of key components of machining equipment.

Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance

Bearing fault diagnosis is of great significance to ensure the safe operation of mechanical equipment. This paper proposes an intelligent fault diagnosis method of rolling bearings based on deep belief network (DBN) with hyperparameter optimization by using parallel computing. Different with traditional diagnosis methods that extract the features manually depending on much prior knowledge about signal processing techniques and diagnostic expertise, DBN extracts fault features automatically by machine learning mechanism. Considering the time consuming problem, parallel computing is adopted to the DBN training process by using a Master/Slave mode to improve the training speed so that the global optimization with Genetic Algorithm and higher diagnosis accuracy can be achieved. Finally, the proposed method is verified with the public datasets provided by Case Western Reserve University (CWRU) with various fault depths in different locations and loads of rolling bearings. The results indicate that the proposed method can identify bearing faults under different conditions correctly which significantly enhances the intelligence of fault classification and reduces the time for parameter selection of deep learning models.

/pdf/a-deep-learning-based-fault-diagnosis-method-with-3r4p2y5jm8.pdf

A Deep Learning Based Fault Diagnosis Method With Hyperparameter Optimization by Using Parallel Computing

The environmental performance of machining processes is of extreme importance in the field of sustainable manufacturing. The development of an environmental impact assessment method is a crucial st...

Lin Li

Papers

Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance

A Deep Learning Based Fault Diagnosis Method With Hyperparameter Optimization by Using Parallel Computing

Exergy-based tool path evaluation method of material and energy flows to support the sustainable-oriented intelligent manufacturing: