With the recent advances in information gathering techniques, product performances and environment/operation conditions can be monitored, and product usage data, including time-dependent product performance feature data and field data (i.e., environmental/operational data), can be continuously collected during the product usage stage. These technologies provide opportunities to improve product design considering product functional performance degradation. The challenge lies in how to assess data of product functional performance degradation for identifying relevant field factors and changing design parameters. An integrated approach for design improvement is developed in this research to transform time-dependent usage data to design information. Many data modeling and analysis techniques such as hierarchal function model, performance feature dimension reduction method, Gaussian mixed model (GMM), and data clustering method are employed in this approach. These methods are used to extract principal features from collected performance features, assess product functional performance degradation, and group field data into meaningful data clusters. The abnormal field data causing severe and rapid product function degradation are obtained based on the field data clusters. A redesign necessity index (RNI) is defined for each design parameter related to severely degraded functions based on the relationships between this design parameter and abnormal field data. An associate relationship matrix (ARM) is constructed to calculate the RNI of each design parameter for identifying the to-be-modified design parameters with high priorities for product improvement. The effectiveness of this new approach is demonstrated through a case study for the redesign of a large tonnage crawler crane.
Simulation of Product Performance Based on Real Product-Usage Information: First Results of Practical Application to Domestic Refrigerators
