Condition monitoring (CM) is the escort of smart manufacturing, which guarantees safety, precision, and efficiency of production process. However, traditional CM capacities with limited storage and computing resources cannot follow the explosive growth of manufacturing data. Empowered by the emerging Internet of Things, cyber-physical system, and cloud computing, cloud manufacturing (CMfg) characterizes a new service-oriented technology, which is reshaping the CM paradigm into an agile, scalable, and interoperable mode. In this article, a CMfg-based CM platform for smart manufacturing is proposed. A 5G wireless communication network-based edge computing system is presented to achieve data acquisition, feature extraction, and emergency response with ultrareliability and low-latency communication. In order to deal with the issue of data management and analysis, a generic open platform communications unified architecture information model oriented to the workshop is proposed in the fog layer. The cloud monitoring platform is constructed encompassing message delivery, data storage, status visualization, and training of artificial intelligence models. Finally, a CMfg-based CM system and a CMfg-based tool wear estimation system are outlined as two industrial cases. The experiments verify the feasibility and effectiveness of the proposed platform.

Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network.

Ball screws play a significant role in maintaining transmission accuracy and stability. Accurate prediction of the remaining useful life (RUL) for the ball screw can provide reliable decision-making information for predictive maintenance. However, the RUL prediction of ball screws with the precision indicator under time-varying operating conditions (OCs) is rarely involved. Moreover, obtaining data for the whole life cycle of each OC is impractical due to time and expenditure constraints. To address the aforementioned issues, this article proposes a hybrid approach for RUL prediction under time-varying OCs. First, a time-varying stochastic model considering the precision indicator is established. Then, the data-driven algorithm is proposed to estimate the unknown parameters with limited data. Finally, the effectiveness of the proposed method in prognostics is experimentally validated through the designed platform and compared with the commonly used methods.

Health prognostics for the machinery is a key objective of condition-based maintenance, with its primary goal being the remaining useful life (RUL) prediction. To efficiently structure prognostic data and address the challenges associated with uncertain operating conditions (OCs), this article introduces the digital twin (DT)-enabled RUL prediction system in smart manufacturing. The system mainly includes three layers, i.e., physical infrastructure layer (PIL), information interaction layer (IIL), and DT service layer (DT-SL). In the PIL, the multisource data are generated from different equipment and then transmitted to the IIL. In the IIL, the DT health prognostics information model is designed to organize the prognostic data in a structured manner. In the DT-SL, the virtual model, prognostic model, and sample generation model are constructed for the visualization of prognostic data, RUL prediction under uncertain OCs, and model validation. Finally, the effectiveness of the proposed system is experimentally demonstrated through two industrial cases, highlighting efficient prognostic data organization and accurate degradation tracking under uncertain OC scenarios.