Under the background of intelligent manufacturing, industrial systems are developing in a more complex and intelligent direction. Equipment maintenance management is facing significant challenges in terms of maintenance workload, system reliability and stability requirements and the overall skill requirements of maintenance personnel. Equipment maintenance management is also developing in the direction of intellectualization. It is important to have a method to construct a domain knowledge graph and to organize and utilize it. As is well known, traditional equipment maintenance is mainly dependent on technicians, and they are required to be very familiar with the maintenance manuals. But it is very difficult to manage and exploit a large quantity of knowledge for technicians in a short time. Hence a method to construct a knowledge graph (KG) for equipment maintenance is proposed to extract knowledge from manuals, and an effective maintenance scheme is obtained with this knowledge graph. Firstly, a joint model based on an enhanced BERT-Bi-LSTM-CRF is put forward to extract knowledge automatically, and a Cosine and Inverse Document Frequency (IDF) based on semantic similarity a presented to eliminate redundancy in the process of the knowledge fusion. Finally, a Decision Support System (DSS) for equipment maintenance is developed and implemented, in which knowledge can be extracted automatically and provide an equipment maintenance scheme according to the requirements. The experimental results show that the joint model used in this paper performs well on Chinese text related to equipment maintenance, with an F1 score of 0.847. The quality of the knowledge graph constructed after eliminating redundancy is also significantly improved.

Sustainability has been attracted extensive attention in manufacturing industry for the increasing excessive resource consumption and serious environmental pollution. The assessment of sustainable manufacturing capability is a vital factor, especially in cloud manufacturing, in which the capability assessment of the manufacturing resources is the premise of selecting the optimal manufacturing services. While the CNC machining tools are the basic manufacturing resources in production process, so the dynamic assessment of their sustainable manufacturing capabilities is investigated in this paper. A set of indicators is established considering manufacturing sustainability and a new dynamic assessment model is proposed based on a modified “vertical and horizontal” method. The model is verified through a case study and it can comprehensively evaluate the capability performance of the CNC machining systems, and also effectively follow the dynamics of their production activities.

Nowadays, the sustainable manufacturing capability of manufacturing devices has attracted more and more attention from academia and industry, in order to coordinate the conflicts between serious environmental impacts and economic benefits. As one kind of advanced manufacturing devices with intelligence, the industrial robot (IR) is an important driving force to make the production activities more efficient, safe and sustainable. A unified sustainable manufacturing capability model for representing IR systems in cloud manufacturing based on ontology was proposed in this paper, so as to solve the description problems in terms of the various capabilities of IR systems, and also to facilitate the factories to effectively manage the IR systems’ manufacturing activities during the whole production life-cycle. The case study and its implementation show the developed ontology model is suitable for all types of IR systems, and can comprehensively reflects their sustainable manufacturing capabilities in real-time.