Abstract: This paper presents a redesign-module identification method based on time-varying product usage data. The method is conducted in three steps：Building a model based on sparse auto-encoder (SAE) neural network using performance feature data from the product health state; Assessing functional performance degradation by using the data during the actual operation; Identifying the redesign module by comparing the difference in functional degradation. Then, a case study of horizontal directional drilling redesign module identification is presented to illustrate feasibility of the proposed method. The result shows the effectiveness of proposed method so that it can identify the weak function modules, while the identified result can provide support for redesign decision-making.

Key words: This paper presents a redesign-module identification method based on time-varying product usage data. The method is conducted in three steps：Building a model based on sparse auto-encoder (SAE) neural network using performance feature data from the product health state; Assessing functional performance degradation by using the data during the actual operation; Identifying the redesign module by comparing the difference in functional degradation. Then, a case study of horizontal directional drilling redesign module identification is presented to illustrate feasibility of the proposed method. The result shows the effectiveness of proposed method so that it can identify the weak function modules, while the identified result can provide support for redesign decision-making.