NDLI: Continuous health condition monitoring: A single Hidden Semi-Markov Model approach

Content Provider	IEEE Xplore Digital Library
Author	Geramifard, O. Jian-Xin Xu Jun-Hong Zhou Xiang Li
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Computer Engineering, National University of Singapore, 10 Kent Ridge, Singapore 119260 (Geramifard, O.; Jian-Xin Xu) \|\| Singapore Institute of Manufacturing Technology, Agency of Science Technology And Research, 71 Nanyang Drive, Singapore 638075 (Jun-Hong Zhou; Xiang Li)
Abstract	In this paper, a single Hidden Semi-Markov Model (HSMM) approach is introduced for continuous health condition monitoring in machinery systems. Contrary to previous attempts in using hidden Markov models in this area which have not provided the relationship between the hidden state values and the physical states, this method provides the aforementioned relationship. In this paper, HSMM is applied as the core model being used in the method in order to increase flexibility of our previously used HMM-based method and consequently its generalization capability. The newly introduced method is compared with our initial HMM-based method which previously outperformed the conventional Artificial Neural Networks approach. Results show that the additional flexibility provided in the new method has improved the performance. As an example, the proposed method is used for tool wear prediction in a CNC-milling machine and results of the study is provided. 482 features are extracted from 7 signals (three force signals, three vibration signals and Acoustic Emission) acquired for each experiment of our dataset. These features include, 48 statistical features extracted from force signals in three directions (16 from each force signal) and 434 averaged wavelet coefficients from all seven signals (62 from each signal). After feature extraction phase, Fisher Discriminant Ratio is applied to find the most discriminant features to construct the prediction model. 38 features out of 482 extracted features are selected to be used in the prediction models. The prediction results are provided for three different cases i.e. cross-validation, diagnostics and prognostics.
Starting Page	1
Ending Page	10
File Size	1407991
Page Count	10
File Format	PDF
ISBN	9781424498284
e-ISBN	9781424498277
DOI	10.1109/ICPHM.2011.6024333
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Condition monitoring Computational modeling Hidden Markov models Markov processes Feature extraction Inference algorithms Joints
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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