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. Decision-by-threshold Decision threshold optimization Fuzzy and probabilistic simultaneous-fault diagnosis Fuzzy neural network Fuzzification Hierarchical fuzzy neural network Multi-label classification One-vs.-all strategy Pairwise coupling strategy Probabilistic neural network Pairwise probabilistic MLC Probabilistic support vector machines Single-fault diagnosis Simultaneous-fault diagnosis Single-label classification Training dataset of single-faults only Training dataset of simultaneous-faults only Test dataset of single-faults only Test dataset of simultaneous-faults only Validation dataset of single-faults only Validation dataset of simultaneous-faults only Binary classifier for the jth fault Probabilistic classifier for the jth fault Probabilistic classifier for the jth fault against the kth fault Number of faults Sample dataset Dataset of single-fault patterns Dataset of simultaneous-fault patterns F-measure Probability Fuzzified sample dataset Fuzzified dataset of single-fault patterns Fuzzified dataset of simultaneous-fault patterns Fuzzified pattern of unknown input x Fuzzified pattern of the ith training data x i jth true fault of x i True fault vector of x i Unknown input pattern ith raw training pattern jth predicted fault of x i Predicted fault vector of x i Decision threshold Optimal decision threshold Heaviside step function Probability vector of faults for a pattern x Probability of the jth fault for a pattern x Probability of the jth fault against the kth fault for a pattern x Defuzzification threshold 
Simultaneous-fault detection based on qualitative symptom descriptions for automotive engine diagnosis