Abstract
The product-service system (PSS) business model has received increasing attention in equipment maintenance studies, as it has the potential to provide high value-added services for equipment users and construct ethical principles for equipment providers to support the implementation of circular economy. However, the PSS providers in equipment industry are facing many challenges when implementing Industry 4.0 technologies. One important challenge is how to fully collect and analyse the operational data of different equipment and diverse users in widely varied conditions to make the PSS providers create innovative equipment management services for their customers. To address this challenge, an active preventive maintenance approach for complex equipment is proposed. Firstly, a novel PSS operation mode was developed, where complex equipment is offered as a part of PSS and under exclusive control by the providers. Then, a solution of equipment preventive maintenance based on the operation mode was designed. A deep neural network was trained to predict the remaining effective life of the key components and thereby, it can pre-emptively assess the health status of equipment. Finally, a real-world industrial case of a leading CNC machine provider was developed to illustrate the feasibility and effectiveness of the proposed approach. Higher accuracy for predicting the remaining effective life was achieved, which resulted in predictive identification of the fault features, proactive implementation of the preventive maintenance, and reduction of the PSS providers’ maintenance costs and resource consumption. Consequently, the result shows that it can help PSS providers move towards more ethical and sustainable directions.
Highlights
With the increasing pressure from global competition and environmental protection, many manufacturing enterprises are making efforts to explore and employ a more sustainable business model aligned with the developing ethical principles of enterprise social responsibility and multi-generational equity for sustainable societies (Luthra and Mangla, 2018; Man and Strandhagen, 2017; Nemoto et al, 2015)
After the deep neural network (DNN)-ELPM is pre-trained, the fine-tuning operation is performed to improve the performance of model fitting
The main objectives of the case study were to test how the equipment maintenance approach could be changed by the proposed operation mode, as well as what improvements were made by using active preventive maintenance
Summary
With the increasing pressure from global competition and environmental protection, many manufacturing enterprises are making efforts to explore and employ a more sustainable business model aligned with the developing ethical principles of enterprise social responsibility and multi-generational equity for sustainable societies (Luthra and Mangla, 2018; Man and Strandhagen, 2017; Nemoto et al, 2015). Recent research achievements, such as lease-oriented opportunistic maintenance for multi-unit systems (Xia et al, 2017), cloud-based augmented reality remote maintenance (Mourtzis et al, 2017), maintenance strategies planning and decision-making for aeroengines (Thomsen et al, 2015), and service-oriented multi-player maintenance grouping strategy (Chang et al, 2019), etc., have provided a solid foundation to enable design and development of PSS-based maintenance approaches for complex equipment These studies provided opportunities for industrial practitioners to apply environmental ethics during the implementation of sustainable production and CE within the Industry 4.0 paradigm (Keitsch, 2018; Mangla et al, 2017; Tunn et al, 2019).
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