International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 03 | Mar -2017
www.irjet.net
e-ISSN: 2395 -0056 p-ISSN: 2395-0072
EFFICIENT FEATURE SELECTION FOR FAULT DIAGNOSIS OF AEROSPACE SYSTEM USING SYNTAX AND SEMANTIC ALGORITHM Sajanvethakumar 123Department
Meena E1, Revathi B2, professor of Computerscience and Engineering)
F3(Assistant
of Computer Science and Engineering, JEPPIAAR SRR Engineering College, Padur, Chennai 603103
----------------------------------------------------------------------------------------------------------------------------------------------------
ABSTRACT:Each and every year, the Aerospace system
However, the task of automatic discovery of information from the repair verbatim may be a non-trivial exercise primarily owing to the following reasons: 1) High-dimension information. In maintenance documents, there are tens of thousands or maybe many thousands of distinct terms or tokens. when elimination of stop words and stemming, the set of options continues to be overlarge for many learning algorithms. 2) unbalanced fault category distribution. In maintenance documents, the number of examples in one fault category (i.e., majority class) is considerably larger than that of the others (i.e., minority classes). Such unbalanced category distributions have exhibit a heavy issue to most classifier learning algorithms that assume a comparatively balanced distribution. 3) unsupervised text mining models. They will not turn out topics that adjust to the user’s existing information. One key reason is that the target functions of topic models, e.g., Latent Dirichlet Allocation, LDA , typically don't correlate well with human judgments.
handles the fault verbatim record database. So the usage of fault verbatim record database is to generate the fault by text, if the airplane does not pass the signal code at correct time when the Airplane starts. It has high dimensional data, learning difficulties and with unstructured verbatim record. Learning difficulties, if the person have little amount of English knowledge, it find difficult to understand. High dimensional data, if the fault having 3 to 4 lines then it may take some time to understand and identify the faults. In proposed system we introduce, Bi-level Feature Extraction Based Text Mining. Bi-level is nothing but the comparison of higher order and lower order. It fault feature derived from both syntax level and semantic level. Syntax level used to overcome the learning difficulties and the semantic level use to convert high dimensional to the low dimensional. It can be used to diagnosis the problem quickly and rectify the problems.
1. INTRODUCTION
This work proposes a bi-level feature extraction-based text mining for fault designation to fulfill the aforesaid challenges by mechanically analyzing the repair verbatim. Our main plan is to extract fault options at syntax and linguistics levels severally so fuse them to realize the required results. Considering the very fact that the extracted options at every level offers a distinct stress to a specific facet of feature spaces and has its deficiencies, the planned feature fusion of two levels could enhance the exactness of fault designation for all fault categories, particularly minority ones. At the syntax level, we have a tendency to propose associate degree improved χ2 statistics (ICHI) to deal with the feature choice of unbalanced information set. First, we have a tendency to overcome the negative result of unbalanced information set by adjusting the feature weight of minority and majority classes. This makes minority categories comparatively distant from the majority ones. Second, we have a tendency to contemplate the Hellinger distance as a choice criterion for feature
Text mining could be a knowledge-intensive task and is gaining a lot of and a lot of attention in many industrial fields, as an example, aerospace, automotive, railway, power, medical, biomedicine, producing, sales and selling sectors. In a railway field, advanced data technologies, such as sensing element networks, RDIF techniques, wireless communication, and net cloud, area unit won’t to monitor the health of the aerospace systems. In the event of malfunctioning, the diagnostic hassle symptoms are generated and transmitted to the watching center info by wired/wireless communications. When each diagnosis episode a repair verbatim is recorded, that consists of a matter description of the mixture of fault symptom (i.e., fault terms), e.g., “Speed Distance Unit (SDU) relevant faults,” a fault symptom e.g., “SDU,” failure modes (i.e., fault classes), and at last corrective actions, e.g., “replaced SDU,” taken to repair its faults.
© 2017, IRJET
|
Impact Factor value: 5.181
|
ISO 9001:2008 Certified Journal
|
Page 1189