The green remanufacturing constitutes a type of recycling form that adapts to ecological and economic requirements and an important part of the advanced manufacturing technology. In order to improve the green remanufacturing capacity of heavy-duty machine tools, in this paper a heavy-duty machine tool module division method for green remanufacturing was proposed. The main lines were based on the four design domains of axiomatic design and innovatively extended to the remanufacturing domain. In the design stage, the process of remanufacturing was considered. The modular clustering algorithm based on atomic theory was employed, which was associated with the correlation and similarity between the design parameters in the structure domain and the remanufacturing domain, for the ideal modules of heavy-duty machine tools to be discovered. Finally, a heavy-duty gantry milling machine is used as an example to verify the validity of the proposed method.

This paper takes on the central heating secondary network and establishes a two-level diagnosis model of leakage fault of heating pipe network based on deep belief network (DBN) under the condition of constant and small supply flow quality regulation. Firstly, a leakage condition hydraulic calculation model of the heating pipe network is established with graph theory, which provides the pressure changes of the pressure monitoring points in the pipe network. Then, the first-level diagnostic model for the leakage of the heating pipe network is designed to diagnose the leaky pipe segment by using a deep belief network. Based on the results of the first-level diagnostic model, each leaky pipe segment is treated as a unit and a second-level diagnosis model is then developed to predict the specific leak location. Finally, the model is verified with a branch-pipe network and a loop-pipe network. Experimental results showed that the firstlevel diagnostic model had a high accuracy rate in the prediction of leaky pipe segments, which was superior to traditional fault diagnosis methods such as BP (Back Propagation Neural Network) and SVM (Support Vector Machines). The second-level diagnostic model can detect the leak location of the leaky pipe with satisfactory results.

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