Simultaneous-fault detection based on qualitative symptom descriptions for automotive engine diagnosis

Practical automotive engine fault diagnosis in an automotive service center is usually performed by analyzing the qualitative symptom descriptions provided by the vehicle owner. However, it is a non-trivial and time-consuming procedure because; (i) the qualitative symptom descriptions are usually collected through a questionnaire containing binary, numerical, and vague data that are difficult to digest; (ii) the engine malfunctioning may not be caused by a single-fault only, but several single-faults simultaneously (i.e., simultaneous-faults). Therefore, automotive mechanic usually requires several days or even weeks to diagnose and fix the engine. To improve this non-trivial and time-consuming procedure of engine fault diagnosis for the mechanic, a new framework of simultaneous-fault diagnosis is proposed in this paper by integrating fuzzification, pairwise probabilistic multi-label classification, and decision-by-threshold. This framework is called fuzzy and probabilistic simultaneous-fault diagnosis (FPSD). Compared to traditional frameworks, FPSD requires much fewer training cases of costly simultaneous-faults while it can probabilistically diagnose both unseen single-faults and simultaneous-faults based on qualitative symptom descriptions. To evaluate the performance of FPSD, a comparative study was conducted over common classification techniques. Experimental results show that the proposed framework can effectively alleviate the aforementioned issues. (C) 2014 Elsevier B.V. All rights reserved.