In the case of a high-valuable asset, the Operation and Maintenance (O&M) phase requires heavy charges and more efforts than the installation (construction) phase, because it has long usage life and any accident of an asset during this period causes catastrophic damage to an industry. Recently, with the advent of emerging Information Communication Technologies (ICTs), we can get the visibility of asset status information during its usage period. It gives us new challenging issues for improving the efficiency of asset operations. One issue is to implement the Condition-Based Maintenance (CBM) approach that makes a diagnosis of the asset status based on wire or wireless monitored data, predicts the assets abnormality, and executes suitable maintenance actions such as repair and replacement before serious problems happen. In this study, we have addressed several aspects of CBM approach: definition, related international standards, procedure, and techniques with the introduction of some relevant case studies that we have carried out.

This study introduces an overall framework for radio frequency identification (RFID) applications in product lifecycle management (PLM). Recently, with emerging technologies related to product identification and wireless mobile tele-communication, PLM provides a new environment that enables lifecycle actors to gather and analyse product lifecycle information, and make efficient decisions without spatial and temporal constraints. However, previous research has paid little attention to how to apply the emerging technologies into PLM. To cope with this limitation, in the current study, RFID applications in PLM are dealt with from technical and business viewpoints. To this end, an overall framework for RFID applications in PLM is proposed. It contains the definition of a product embedded information device (PEID) that uses RFID technology, its specification and operation scenarios for each application. Furthermore, application issues and their overall procedures are introduced for each lifecycle phase.

Wire + arc additive manufacturing (WAAM) utilizes a welding arc as a heat source and a metal wire as a feedstock. In recent years, WAAM has attracted significant attention in the manufacturing industry owing to its advantages: (1) high deposition rate, (2) low system setup cost, (3) wide diversity of wire materials, and (4) sustainability for constructing large-sized metal structures. However, owing to the complexity of arc welding in WAAM, more research efforts are required to improve its process repeatability and advance part qualification. This study proposes a methodology to detect defects of the arch welding process in WAAM using images acquired by a high dynamic range camera. The gathered images are preprocessed to emphasize features and used for an artificial intelligence model to classify normal and abnormal statuses of arc welding in WAAM. Owing to the shortage of image datasets for defects, transfer learning technology is adopted. In addition, to understand and check the basis of the model’s feature learning, a gradient-weighted class activation mapping algorithm is applied to select a model that has the correct judgment criteria. Experimental results show that the detection accuracy of the metal transfer region-of-interest (RoI) reached 99%, whereas that of the weld-pool and bead RoI was 96%.

The closed-loop product life cycle management (closed-loop PLM) system focuses on tracking and managing the information of whole product life cycle, with possible feedback of information to product life cycle phases. It provides opportunities to reduce the inefficiency of life cycle operations and gain competitiveness. Thanks to the advent of hardware and software related to product identification technologies, e.g. radio frequency identification (RFID) technology, recently the closed-loop PLM has been highlighted as a tool of companies to enhance the performance of their business models. However, implementing the PLM system requires a high level of coordination and integration. To this end, it is prerequisite to investigate what are the main components for closed-loop PLM and how they are related to each other. To address this need, the curent study focuses on the system architecture of closed-loop PLM with respect to business model, hardware, and software.

Thanks to recent technology related to product monitoring and data communication, it is now possible for a company to gather various kinds of product usage data during its operation in a ubiquitous way. However, the application of the newly gathered data is still vague and immature. If the data is transformed into appropriate information or knowledge, it would be possible to improve products through design modification, maintenance enhancement, elaborate end-of-life decision and so on. This article deals with the application of product usage data to support design modification. Design modification can be achieved by finding and fixing defective design parameters and affecting factors. This done by a complex procedure which is composed of several steps including diverse data processing technique such as function structure model, degradation scenario, function performance degradation evaluation, clustering and relation matrix. The proposed method assesses the status of working product, classifies the field data (i.e. working environment/operation) according to the status and identifies suspicious field data causing the poor working status. Then, the suspicious field data is correlated with design parameters; thus highly defective design parameters can be identified. For the product improvement, the defective design parameters should be reviewed and modified with a high priority. The proposed method is validated by a case study on a locomotive braking system. This work shows the possibility to transform product usage data into information for product improvement, which can serve as a guideline for product usage data application for other product improvement through product usage data.