As a successful business strategy for enhancing environmental sustainability and decreasing the natural resource consumption of societies, the product-service system (PSS) has raised significant interests in the academic and industrial community. However, with the digitisation of the industry and the advancement of multisensory technologies, the PSS providers face many challenges. One major challenge is how the PSS providers can fully capture and efficiently analyse the operation and maintenance big data of different products and different customers in different conditions to obtain insights to improve their production processes, products and services. To address this challenge, a new operation mode and procedural approach are proposed for operation and maintenance of bigger cluster products, when these products are provided as a part of PSS and under exclusive control by the providers. The proposed mode and approach are driven by lifecycle big data of large cluster products and employs deep learning to train the neural networks to identify the fault features, thereby monitoring the products’ health status. This new mode is applied to a real case of a leading CNC machine provider to illustrate its feasibility. Higher accuracy and shortened time for fault prediction are realised, resulting in the provider’s saving of the maintenance and operation cost.

作为提高环境可持续性和减少社会自然资源消耗的成功商业策略，产品服务系统（PSS）在学术界和工业界引起了广泛兴趣。但是，随着行业的数字化和多传感器技术的发展，PSS提供商面临许多挑战。一个主要的挑战是PSS提供商如何在不同条件下全面捕获和有效分析不同产品和不同客户的运营和维护大数据，以获得改进生产流程，产品和服务的见解。为了应对这一挑战，为大型集群产品的运营和维护提出了一种新的运营模式和程序方法，当这些产品作为PSS的一部分提供并由提供商独家控制时。所提出的模式和方法是由大型集群产品的生命周期大数据驱动的，并采用深度学习来训练神经网络以识别故障特征，从而监视产品的健康状态。此新模式应用于领先的CNC机床供应商的实际案例，以说明其可行性。实现了更高的准确性和更短的故障预测时间，从而使提供商节省了维护和运营成本。此新模式应用于领先的CNC机床供应商的实际案例，以说明其可行性。实现了更高的准确性和更短的故障预测时间，从而使提供商节省了维护和运营成本。此新模式应用于领先的CNC机床供应商的实际案例，以说明其可行性。实现了更高的准确性和更短的故障预测时间，从而使提供商节省了维护和运营成本。