TY - GEN
T1 - Root Cause Identification Approach Based on FP-Growth for Product Quality Accident
AU - Li, Pengyu
AU - He, Yihai
AU - Liu, Fengdi
AU - Hou, Wenkui
AU - Duan, Panting
AU - Zhao, Yixiao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/4
Y1 - 2019/1/4
N2 - The Product Quality Accident (PQA) is a kind of accident mainly caused by product quality defects originated in design and production. To decrease the number and the severity of PQA in usage is a routine task of quality and reliability engineer. Especially with the advent of the era of intelligent manufacturing and big data, the functional structure of product is becoming increasingly complicated and the dimension of big data in product lifecycle is high. As a result, it is difficult to artificially identify the root cause of PQA by traditional method. Therefore, this paper proposes a novel method based on an advanced data mining algorithm for root cause identification. Firstly, the quality accident formation mechanism is introduced in detail. Secondly, with the aid of the domain mapping theory, PQA relevance tree is contributed. Thirdly, with the setting of reasonable support and confidence, the FP-Growth algorithm is used to further mine the relevance rules and construct a complete PQA relevance tree, which could reduce the ambiguity of PQA root cause identification. Finally, the effectiveness of the proposed technique is verified by a root cause analysis example of an engine quality accident.
AB - The Product Quality Accident (PQA) is a kind of accident mainly caused by product quality defects originated in design and production. To decrease the number and the severity of PQA in usage is a routine task of quality and reliability engineer. Especially with the advent of the era of intelligent manufacturing and big data, the functional structure of product is becoming increasingly complicated and the dimension of big data in product lifecycle is high. As a result, it is difficult to artificially identify the root cause of PQA by traditional method. Therefore, this paper proposes a novel method based on an advanced data mining algorithm for root cause identification. Firstly, the quality accident formation mechanism is introduced in detail. Secondly, with the aid of the domain mapping theory, PQA relevance tree is contributed. Thirdly, with the setting of reasonable support and confidence, the FP-Growth algorithm is used to further mine the relevance rules and construct a complete PQA relevance tree, which could reduce the ambiguity of PQA root cause identification. Finally, the effectiveness of the proposed technique is verified by a root cause analysis example of an engine quality accident.
KW - Axiomatic design
KW - FP-Growth algorithm
KW - Product quality accidents
KW - Root cause analysis
UR - https://www.scopus.com/pages/publications/85061834234
U2 - 10.1109/PHM-Chongqing.2018.00157
DO - 10.1109/PHM-Chongqing.2018.00157
M3 - 会议稿件
AN - SCOPUS:85061834234
T3 - Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
SP - 876
EP - 881
BT - Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
A2 - Ding, Ping
A2 - Li, Chuan
A2 - Yang, Shuai
A2 - Ding, Ping
A2 - Sanchez, Rene-Vinicio
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
Y2 - 26 October 2018 through 28 October 2018
ER -