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Purpose – Product-service system (PSS) has been attracting attentions of global manufacturing to providing high-value added services in addition to their traditional product development and manufacturing business. For this reason, it is of great importance to research PSS. The purpose of this paper is to establish a systematic strategy and a system tool for PSS design. Design/methodology/approach – A requirement-driven product-service system (RdPPS) is developed using requirements analysis and knowledge management technologies. A framework is proposed to support RdPPS by providing tools and methods for requirement analysis and processing, formalization of PSS by ontology-based knowledge representation, reasoning method for PSS solution finding, and solution optimizing and assessing. Finally, the design support strategies for RdPPS are investigated to demonstrate the usability and functioning of the developed system. Findings – Many conventional design methods did not consider the influence of customer requirements (CRs) during the planning phase of PSS design. Moreover, a broader range of knowledge is required to PSS design, since both products and services are considered. Research limitations/implications – This research provides a solid foundation for PSS, and promotes an effective means for PSS design. Originality/value – A RdPSS is presented. CRs are considered during the design phase of PSS as well as both product and service knowledge.

Along with the number and the functional complexity of machines increase in the intelligent manufacturing system, the probability of faults will increase, which may lead to huge economic losses. Traditional passive or regular maintenance methods of solving the faults have the problems of low efficiency and huge resource consumption. Besides, traditional maintenance methods mostly contain single model, so all the prognostics and maintenance tasks of the intelligent manufacturing system can hardly be addressed at the same time. Therefore, this paper proposes a novel predictive maintenance (PDM) method based on the improved deep adversarial learning (LSTM-GAN). The long-short-term memory (LSTM) network can solve the disadvantage of vanishing gradients and the mode collapse from the generative adversarial network (GAN). The method can not only avoid the mode collapse of GAN but also realize the self-detection of abnormal data. Meanwhile, the predictive maintenance model includes two prediction models and a maintenance decision model. The prediction models can predict the state of the machine and the fault of the machine in advance. Then the maintenance decision model will arrange maintenance personnel and offer a plan of maintenance. Finally, a case study about predictive maintenance using LSTM-GAN in the intelligent manufacturing system is presented. The fault prediction accuracy of LTSM-GAN is as high as 99.68%. With the comparison between LSTM-GAN and other traditional methods, LSTM-GAN shows priority both in accuracy and efficiency. Moreover, the proposed PDM can reduce maintenance costs and downtime so that the life of machines in the intelligent manufacturing system will extend.

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