Design modification supporting method based on product usage data in closed-loop PLM

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.