Paper: 351456
Title: A Multi-Experts Methodology for Early Detection and Diagnosis of Mechanical Defects

-------------------- review 1 --------------------
---------------------------- REVIEW 1 --------------------------
PAPER: 15
TITLE: A Multi-Experts Methodology for Early Detection and Diagnosis of Mechanical Defects
 
OVERALL RATING: 3 (strong accept) 
REVIEWER'S CONFIDENCE: 3 (high) 
Relevance to this conference: 4 (good)
Originality/Uniqueness: 3 (fair)
English readability: 5 (excellent)
Paper organization/presentation: 5 (excellent)
Has good survey been done?: 4 (good)


Very well-written paper.
My only remark is about figures - fig. 2, 3 and 8 are not readable and should be enlarged. 



-------------------- review 2 --------------------
---------------------------- REVIEW 2 --------------------------
PAPER: 15
TITLE: A Multi-Experts Methodology for Early Detection and Diagnosis of Mechanical Defects
 
OVERALL RATING: 2 (accept) 
REVIEWER'S CONFIDENCE: 2 (medium) 
Relevance to this conference: 4 (good)
Originality/Uniqueness: 4 (good)
English readability: 3 (fair)
Paper organization/presentation: 4 (good)
Has good survey been done?: 4 (good)


The paper presents original neural based multi-expert architecture for early detection of mechanical defects. It also provides links to the similar works. I am not an expert in English, but in my opinion article reads pretty hard, I believe that many expressions could be simplified. 



-------------------- review 3 --------------------
---------------------------- REVIEW 3 --------------------------
PAPER: 15
TITLE: A Multi-Experts Methodology for Early Detection and Diagnosis of Mechanical Defects
 
OVERALL RATING: 2 (accept) 
REVIEWER'S CONFIDENCE: 3 (high) 
Relevance to this conference: 4 (good)
Originality/Uniqueness: 3 (fair)
English readability: 4 (good)
Paper organization/presentation: 4 (good)
Has good survey been done?: 4 (good)


The paper presents a multi-expert method for mechanical defect detection.
It uses some practical/real data, which is good. But it is not clear
how novel the method is. The paper can be accepted if the following minor 
changes are made: it would be beneficial to mention more relevant work in detection 
with soft computing techniques, for example,

V. Mitra, Chia-Jiu Wang, and S. Banerjee, "Lidar detection of underwater objects using a neuro-SVM-based architecture," IEEE Transactions on Neural Networks, no.17, pp.717-731, 2006. 

L.P. Wang and X.J. Fu, Data Mining with Computational Intelligence, Springer, Berlin, 2005.