34 research outputs found
Kernel Belief Propagation
We propose a nonparametric generalization of belief propagation, Kernel
Belief Propagation (KBP), for pairwise Markov random fields. Messages are
represented as functions in a reproducing kernel Hilbert space (RKHS), and
message updates are simple linear operations in the RKHS. KBP makes none of the
assumptions commonly required in classical BP algorithms: the variables need
not arise from a finite domain or a Gaussian distribution, nor must their
relations take any particular parametric form. Rather, the relations between
variables are represented implicitly, and are learned nonparametrically from
training data. KBP has the advantage that it may be used on any domain where
kernels are defined (Rd, strings, groups), even where explicit parametric
models are not known, or closed form expressions for the BP updates do not
exist. The computational cost of message updates in KBP is polynomial in the
training data size. We also propose a constant time approximate message update
procedure by representing messages using a small number of basis functions. In
experiments, we apply KBP to image denoising, depth prediction from still
images, and protein configuration prediction: KBP is faster than competing
classical and nonparametric approaches (by orders of magnitude, in some cases),
while providing significantly more accurate results
๋ง๋ฅด์ฝํ ๋๋ค ํ๋ ํ์ต ๋ฐ ์ถ๋ก ๊ณผ ๊ทธ๋ํ ๋ผ์๋ฅผ ํ์ฉํ ๊ณต์ ์ด์ ๊ฐ์ง ๋ฐ ์ง๋จ ๋ฐฉ๋ฒ๋ก
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ํํ์๋ฌผ๊ณตํ๋ถ, 2019. 2. ์ด์๋ณด.Fault detection and diagnosis (FDD) is an essential part of safe plant operation. Fault detection refers to the process of detecting the occurrence of a fault quickly and accurately, and representative methods include the use of principal component analysis (PCA), and autoencoders (AE). Fault diagnosis is the process of isolating the root cause node of the fault, then determining the fault propagation path to identify the characteristic of the fault. Among the various methods, data-driven methods are the most widely-used, due to their applicability and good performance compared to analytical and knowledge-based methods. Although many studies have been conducted regarding FDD, no methodology for conducting every step of FDD exists, where the fault is effectively detected and diagnosed. Moreover, existing methods have limited applicability and show limited performance. Previous fault detection methods show loss of variable characteristics in dimensionality reduction methods and have large computational loads, leading to poor performance for complex faults. Likewise, preceding fault diagnosis methods show inaccurate fault isolation results, and biased fault propagation path analysis as a consequence of implementing knowledge-based characteristics for construction of digraphs of process variable relationships. Thus a comprehensive methodology for FDD which shows good performance for complex faults and variable relationships, is required.
In this study, an efficient and effective comprehensive FDD methodology based on Markov random fields (MRF) modelling is proposed. MRFs provide an effective means for modelling complex variable relationships, and allows efficient computation of marginal probability of the process variables, leading to good performance regarding FDD.
First, a fault detection framework for process variables, integrating the MRF modelling and structure learning with iterative graphical lasso is proposed. Graphical lasso is an algorithm for learning the structure of MRFs, and is applicable to large variable sets since it approximates the MRF structure by assuming the relationships between variables to be Gaussian. By iteratively applying the graphical lasso to monitored variables, the variable set is subdivided into smaller groups, and consequently the computational cost of MRF inference is mitigated allowing efficient fault detection. After variable groups are obtained through iterative graphical lasso, they are subject to the MRF monitoring framework that is proposed in this work. The framework obtains the monitoring statistics by calculating the probability density of the variable groups through kernel density estimation, and the monitoring limits are obtained separately for each group by using a false alarm rate of 5%.
Second, a fault isolation and propagation path analysis methodology is proposed, where the conditional marginal probability of each variable is computed via inference, then is used to calculate the conditional contribution of individual variables during the occurrence of a fault. Using the kernel belief propagation (KBP) algorithm, which is an algorithm for learning and inferencing MRFs comprising continuous variables, the parameters of MRF are trained using normal process data, then the individual conditional contribution of each variable is calculated for every sample of the fault process data. By analyzing the magnitude and reaction speed of the conditional contribution of individual variables, the root fault node can be isolated and the fault propagation path can be determined effectively.
Finally, the proposed methodology is verified by applying it to the well-known Tennessee Eastman process (TEP) model. Since the TEP has been used as a benchmark process over the past years for verifying various FDD methods, it serves the purpose of performance comparison. Also, since it consists of multiple units and has complex variable relationships such as recycle loops, it is suitable for verifying the performance of the proposed methodology. Application results show that the proposed methodology performs better compared to state-of-the-art FDD algorithms, in terms of both fault detection and diagnosis. Fault detection results showed that all 28 faults designed inside the TEP model were detected with a fault detection accuracy of over 95%, which is higher than any other previously proposed fault detection method. Also, the method showed good fault isolation and propagation path analysis results, where the root-cause node for every fault was detected correctly, and the characteristics of the initiated faults were identified through fault propagation path analysis.๊ณต์ ์ด์์ ๊ฐ์ง ๋ฐ ์ง๋จ ์์คํ
์ ์์ ํ ๊ณต์ ์ด์์ ํ์์ ์ธ ๋ถ๋ถ์ด๋ค. ์ด์ ๊ฐ์ง๋ ์ด์์ด ๋ฐ์ํ์ ๊ฒฝ์ฐ ์ฆ๊ฐ์ ์ผ๋ก ์ด๋ฅผ ์ ํํ๊ฒ ๊ฐ์งํ๋ ํ๋ก์ธ์ค๋ฅผ ์๋ฏธํ๋ฉฐ, ๋ํ์ ์ธ ๋ฐฉ๋ฒ์ผ๋ก๋ ์ฃผ์ฑ๋ถ ๋ถ์ ๋ฐ ์คํ ์ธ์ฝ๋๋ฅผ ํ์ฉํ ๊ฐ์ง ๋ฐฉ๋ฒ๋ก ์ด ์๋ค. ์ด์ ์ง๋จ์ ๊ฒฐํจ์ ๊ทผ๋ณธ ์์ธ์ด ๋๋ ๋
ธ๋๋ฅผ ๊ฒฉ๋ฆฌํ๊ณ , ์ด์์ ์ ํ ๊ฒฝ๋ก๋ฅผ ํ์งํ์ฌ ์ด์์ ํน์ฑ์ ์๋ณํ๋ ํ๋ก์ธ์ค์ด๋ค. ๊ณต์ ์ด์์ ๊ฐ์ง ๋ฐ ์ง๋จ ๋ฐฉ๋ฒ๋ก ์๋ ๋ชจ๋ธ ๋ถ์ ๋ฐฉ๋ฒ๋ก , ์ง์ ๊ธฐ๋ฐ ๋ฐฉ๋ฒ๋ก ๋ฑ์ ๋ค์ํ ๋ฐฉ๋ฒ๋ก ์ด ์์ง๋ง, ๊ณต์ ์ ๋ํ ์ ์ฉ ๊ฐ๋ฅ์ฑ๊ณผ ์ฑ๋ฅ ์ธก๋ฉด์์ ๊ฐ์ฅ ์ ์ฉํ๋ค๊ณ ์๋ ค์ ธ ์๋ ๋ฐ์ดํฐ ๊ธฐ๋ฐ ๋ฐฉ๋ฒ๋ก ์ด ๋๋ฆฌ ํ์ฉ๋๊ณ ์๋ค. ๊ณต์ ์ด์์ ๊ฐ์ง ๋ฐ ์ง๋จ์ ๋ํ ๋ฐ์ดํฐ ๊ธฐ๋ฐ ๋ฐฉ๋ฒ๋ก ์ ๋ค๋ฐฉ๋ฉด์ผ๋ก ์ฐ๊ตฌ๋์ด ์์ง๋ง, ์ด์ ๊ฐ์ง ๋ฐ ์ง๋จ์ ๋ชจ๋ ํจ๊ณผ์ ์ผ๋ก ์ํํ ์ ์๋ ๋ฐฉ๋ฒ๋ก ์ ์์์ ๋ถ๊ณผํ๋ฉฐ, ์กด์ฌํ๊ณ ์๋ ๋ฐฉ๋ฒ๋ก ๋ค ์ญ์ ๋ ๋ถ์ผ ๋ชจ๋์์ ์ข์ ์ฑ๋ฅ์ ๋ณด์ฌ์ฃผ๊ณ ์๋ ๊ฒฝ์ฐ๋ ์๋ค. ์ด๋ ๊ธฐ์กด ๋ฐฉ๋ฒ๋ก ๋ค์ ์ ์ฉ ๊ฐ๋ฅ์ฑ์ด ์ ํ๋์ด ์์ผ๋ฉฐ ๊ณต์ ์ ์ ์ฉ์ ์ ํ๋ ์ฑ๋ฅ์ ๋ณด์ฌ์ฃผ๊ธฐ ๋๋ฌธ์ด๋ค. ์ด์ ๊ฐ์ง์ ๊ฒฝ์ฐ, ๋์ฉ๋์ ๋ฐ์ดํฐ๋ฅผ ์ฒ๋ฆฌํ ๋ ๋ฐ์ํ๋ ๊ณผ๋ถํ๋ก ์ธํ ๊ฐ์ง ๋ฅ๋ ฅ์ ์ ํ, ์ฐจ์ ์ถ์ ๋ฐฉ๋ฒ๋ก ๋ค์ ์ฌ์ฉํ ์ ์ด์ ๋ฐ๋ฅธ ๋ณ์ ํน์ฑ ๋ฐ์์ ๋ถ์ ํ์ฑ, ๊ทธ๋ฆฌ๊ณ ์ถ์๋ ์ฐจ์์์์ ๊ณ์ฐ์ผ๋ก ์ธํ์ฌ ๋ณตํฉ์ ์ธ ํํ์ ์ด์์ ๊ฐ์งํด ๋ด์ง ๋ชปํ๋ ๋ฌธ์ ๋ฑ์ด ์๋ค. ์ด์ ์ง๋จ์ ๊ฒฝ์ฐ ์ด์์ ์์ธ์ด ๋๋ ๋
ธ๋์ ๊ฒฉ๋ฆฌ ๋ฐ ์ด์ ์ ํ ๊ฒฝ๋ก์ ๋ํ ๋ถ์์ด ๋ถ์ ํํ ๊ฒฝ์ฐ๊ฐ ๋ง์๋ฐ, ์ด๋ ์ฐจ์ ์ถ์๋ก ์ธํ์ฌ ๊ณต์ ๋ณ์์ ํน์ฑ์ด ์์ค๋๋ ์ฑ์ง์ด ์๊ณ , ๋ฐฉํฅ์ฑ ๊ทธ๋ํ๋ฅผ ํ์ฉํ ์ ๊ณต์ ์ ๋ํ ์ ํ ์ง์์ ์ ์ฉํจ์ผ๋ก์จ ํธํฅ๋ ์ด์ ์ง๋จ ๊ฒฐ๊ณผ๊ฐ ๋ํ๋๋ ๊ฒฝ์ฐ๋ค์ด ๋ฐ์ํ๊ธฐ ๋๋ฌธ์ด๋ค. ๊ธฐ์กด ๋ฐฉ๋ฒ๋ก ๋ค์ ๋ํ ์ด๋ฌํ ํ๊ณ์ ๋ค์ ๊ณ ๋ คํด ๋ดค์๋, ๋ณ์ ๊ฐ๊ฐ์ ํน์ฑ์ด ์์ค๋์ง ์๋๋กํ์ฌ ํจ๊ณผ์ ์ผ๋ก ์ด์์ ๋ํ ๊ฐ์ง์ ์ง๋จ์ ๋ชจ๋ ์ํํด ๋ผ ์ ์์ผ๋ฉด์๋, ๊ณ์ฐ์์ ํจ์จ์ฑ์ ๊ฐ์ถ, ์ด์ ๊ฐ์ง ๋ฐ ์ง๋จ์ ๋ํ ํตํฉ๋ ๋ฐฉ๋ฒ๋ก ์ ๊ฐ๋ฐ์ด ์๊ธํ๋ค๊ณ ํ ์ ์๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ๋ง๋ฅด์ฝํ ๋๋ค ํ๋ ๋ชจ๋ธ๋ง๊ณผ ๊ทธ๋ํ ๋ผ์๋ฅผ ๊ธฐ๋ฐ์ผ๋กํ์ฌ, ์ด์์ ๋ํ ๊ฐ์ง ๋ฐ ์ง๋จ์ ๋ชจ๋ ์ํํด ๋ผ ์ ์๋ ํตํฉ์ ์ธ ๊ณต์ ๋ชจ๋ํฐ๋ง ๋ฐฉ๋ฒ๋ก ์ ์ ์ํ๋ค. ๋ง๋ฅด์ฝํ ๋๋ค ํ๋๋ ๋น์ ํ์ ์ด๊ณ ๋น์ ๊ท์ ์ธ ๋ณ์ ๊ด๊ณ๋ฅผ ํจ๊ณผ์ ์ผ๋ก ๋ชจ๋ธ๋งํ ์ ์๊ฒ ํด์ฃผ๊ณ , ์ด์ ๋ฐ์ ์ํฉ์์์ ๋ชจ๋ํฐ๋ง ํต๊ณ๊ฐ ๊ณ์ฐ์์ ๊ฐ ๋ณ์์ ํน์ฑ์ ๋ฐ์ํ์ฌ ํ๋ฅ ๊ณ์ฐ์ ํด ๋ผ ์ ์๊ธฐ ๋๋ฌธ์ ํจ๊ณผ์ ์ธ ์ด์ ๊ฐ์ง ๋ฐ ์ง๋จ ์๋จ์ด ๋๋ค. ๊ธฐ๋ณธ์ ์ผ๋ก ๋ง๋ฅด์ฝํ ๋๋ค ํ๋๋ ํ๋ฅ ๊ฐ ๊ณ์ฐ์์ ๋ถํ๊ฐ ํฌ์ง๋ง, ๋ณธ ์ฐ๊ตฌ์์๋ ๊ทธ๋ํ ๋ผ์ ๋ฐฉ๋ฒ๋ก ์ ์ถ๊ฐ์ ์ผ๋ก ํจ๊ป ํ์ฉํ์ฌ ๊ณ์ฐ ์์ ๋ถํ๋ฅผ ์ค์ด๊ณ ํจ์จ์ ์ผ๋ก ์ด์ ๊ฐ์ง ๋ฐ ์ง๋จ์ ํด๋ผ ์ ์๋๋ก ํ์๋ค. ๋ณธ ์ฐ๊ตฌ์์ ์ ์๋ ๋ด์ฉ๋ค์ ๋ค์๊ณผ ๊ฐ๋ค.
์ฒซ์งธ, ๊ณต์ ๋ณ์๋ฅผ ๋ง๋ฅด์ฝํ ๋๋ค ํ๋ ํํ๋ก ๋ชจ๋ธ๋งํ๊ณ , ๊ทธ๋ํ ๋ผ์๋ฅผ ํ์ฉํด ๋ง๋ฅด์ฝํ ๋๋ค ํ๋์ ๊ตฌ์กฐ๋ฅผ ์ป์ ์ ์๋ ๋ฐฉ๋ฒ๋ก ์ ์ ์ํ์๋ค. ๊ทธ๋ํ ๋ผ์๋ ๋ง๋ฅด์ฝํ ๋๋ค ํ๋์ ๊ตฌ์กฐ๋ฅผ ํ์
ํ๊ธฐ ์ํ ๋ฐฉ๋ฒ๋ก ์ธ๋ฐ, ๋ณ์ ๊ฐ์ ๊ด๊ณ๋ฅผ ๊ฐ์ฐ์ค ํจ์์ ํํ๋ก ๊ฐ์ ํ๊ธฐ ๋๋ฌธ์ ๋ค๋ณ์ ์์คํ
์์๋ ํจ์จ์ ์ผ๋ก ๊ทธ๋ํ ๊ตฌ์กฐ๋ฅผ ํ์
ํ ์ ์๋๋ก ํด์ค๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ๋ฐ๋ณต์ ๊ทธ๋ํ ๋ผ์๋ฅผ ์ ์ํ์ฌ ๋ชจ๋ ๊ณต์ ๋ณ์๋ค์ด ์๊ด๊ด๊ณ๊ฐ ๋์ ๋ณ์ ์ง๋จ์ผ๋ก ๋ฌถ์ผ ์ ์๋๋ก ํ์๋ค. ์ด๋ฅผ ํ์ฉํ๋ฉด ์ ์ฒด ๊ณต์ ๋ณ์ ์ง๋จ์ ๋ค์์ ์์ง๋จ์ผ๋ก ๋ถ๋ฅํ๊ณ ๊ฐ๊ฐ์ ๋ํ ๊ทธ๋ํ ๊ตฌ์กฐ๋ฅผ ํ์
ํ ์ ์๊ฒ ๋๋๋ฐ, ํฌ๊ฒ ๋ ๊ฐ์ง์ ํจ๊ณผ๋ฅผ ์ป์ ์ ์๋ค. ์ฐ์ ์ ์ผ๋ก ๋ง๋ฅด์ฝํ ๋๋ค ํ๋ ํ๋ฅ ๊ณ์ฐ์ ๋์์ด ๋๋ ๋ณ์์ ๊ฐ์๋ฅผ ์ค์ฌ์ค์ผ๋ก์จ ๊ณ์ฐ ๋ถํ๋ฅผ ์ค์ด๊ณ ํจ์จ์ ์ธ ์ด์ ๊ฐ์ง๊ฐ ์ด๋ฃจ์ด์ง ์ ์๋๋ก ํ๋ค. ๋ํ ์๊ด๊ด๊ณ๊ฐ ๋์ ์ง๋จ๋ผ๋ฆฌ ๋ฌถ์ฌ์ ๋ชจ๋ธ๋ง ๋ ๊ทธ๋ํ๋ฅผ ํ์ฉํ์ฌ ์ด์์ ์ง๋จ ๊ณผ์ ์์ ๊ณต์ ๋ณ์ ๊ฐ์ ๊ด๊ณ ํ์
๋ฐ ์ ํ ๊ฒฝ๋ก ๋ถ์์ ์ฉ์ดํ๋๋ก ํด์ค๋ค.
๋ ๋ฒ์งธ๋ก, ๋ง๋ฅด์ฝํ ๋๋ค ํ๋์ ํ๋ฅ ์ถ๋ก ์ ๊ธฐ๋ฐ์ผ๋ก ํ์ฌ ํจ๊ณผ์ ์ผ๋ก ์ด์ ๊ฐ์ง๊ฐ ์ด๋ฃจ์ด์ง ์ ์๋๋ก ํ๋ ๋ฐฉ๋ฒ๋ก ์ ์ ์ํ์๋ค. ๋ฐ๋ณต์ ๊ทธ๋ํ ๋ผ์๋ฅผ ํตํด ์ป์ด์ง ๋ค์์ ๋ณ์ ์์ง๋จ์ ๋ํ์ฌ ๊ฐ๊ฐ ํ๋ฅ ์ถ๋ก ์ ์ ์ฉํ์ฌ ์ด์ ๊ฐ์ง๋ฅผ ์งํํ๊ฒ ๋๋๋ฐ, ์ ์๋ ๋ฐฉ๋ฒ๋ก ์์๋ ์ปค๋ ๋ฐ๋ ์ถ์ ๋ฐฉ๋ฒ๋ก ์ ํ์ฉํ์๋ค. ์ ์ ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ์ฌ ๊ฐ ๋ณ์๋ค์ ๋ํ ์ปค๋ ๋ฐ๋์ ๋์ญํญ์ ํ์ตํ๊ณ , ์ด์ ๋ฐ์ดํฐ๊ฐ ๋ฐ์ํ ์ ์ด๋ฅผ ํ์ฉํ ์ปค๋ ๋ฐ๋ ์ถ์ ๋ฒ์ ์ฌ์ฉํ์ฌ ์ด์๊ฐ์ ํต๊ณ์น๋ฅผ ๊ณ์ฐํ๊ฒ ๋๋ค. ์ด๋ ํ์ ์ง๋จ์จ์ 5%๋ก ๊ฐ์ ํ์ฌ ๊ฐ๊ฐ์ ์์ง๋จ์ ๋ํ ๊ณต์ ๊ฐ์ง ๊ธฐ์ค์ ์ ์ค์ ํ์๊ณ , ์ด์๊ฐ์ ํต๊ณ์น๊ฐ ๊ณต์ ๊ฐ์ ๊ธฐ์ค์ ๋ณด๋ค ๋ฎ๊ฒ ๋ ๊ฒฝ์ฐ ์ด์์ด ๊ฐ์ง๋๋ค.
์ธ ๋ฒ์งธ๋ก, ์ด์ ๋ฐ์ ์ ์์ธ์ด ๋๋ ๋ณ์์ ๊ฒฉ๋ฆฌ ๋ฐ ์ด์ ์ ํ ๊ฒฝ๋ก ๋ถ์์ ํจ๊ณผ์ ์ผ๋ก ์ํํ ์ ์๋ ๋ฐฉ๋ฒ๋ก ์ ์ ์ํ์๋ค. ์ ์๋ ๋ฐฉ๋ฒ๋ก ์์๋ ๋ง๋ฅด์ฝํ ๋๋ค ํ๋์ ํ๋ฅ ์ถ๋ก ๊ณผ์ ์ ํ์ฉํ์ฌ ์ด์ ๋ฐ์ ์ ๊ฐ ๋ณ์์ ์กฐ๊ฑด๋ถ ํ๊ณ ํ๋ฅ ์ ๊ณ์ฐํ๊ณ , ์ด๋ฅผ ํ์ฉํด ์๋กญ๊ฒ ์ ์๋ ์กฐ๊ฑด๋ถ ๊ธฐ์ฌ๋ ๊ฐ์ ๊ณ์ฐํ์ฌ, ์ด์์ ๋ํ ๊ฐ ๋ณ์์ ๊ธฐ์ฌ๋๋ฅผ ํ์
ํ ์ ์๋๋ก ํ๋ค. ์ด ๊ณผ์ ์์๋ ์ปค๋ ์ ๋ขฐ๋ ์ ํ ๋ฐฉ๋ฒ๋ก ์ด ์ฌ์ฉ๋๋๋ฐ, ์ด๋ ์ฐ์ ๋ณ์๋ฅผ ๊ฐ์ง๋ ๋ง๋ฅด์ฝํ ๋๋ค ํ๋์ ๋ํ์ฌ ํ๋ฅ ์ถ๋ก ์ ์ํํ ์ ์๋๋ก ํ๋ ๋ฐฉ๋ฒ๋ก ์ด๋ค. ์ปค๋ ์ ๋ขฐ๋ ์ ํ๋ฒ์ ์ฌ์ฉํ๋ฉด ์ ์ ์ํ์ ๊ณต์ ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ์ฌ ๋ง๋ฅด์ฝํ ๋๋ค ํ๋๋ฅผ ๊ตฌ์ฑํ๋ ํ๋ผ๋ฏธํฐ ๊ฐ๋ค์ ํ์ตํ๊ณ , ์ด์ ๋ฐ์์ ์ด์ ๋ฐ์ดํฐ์ ๋ํ์ฌ ๊ฐ ๋ณ์์ ์กฐ๊ฑด๋ถ ๊ธฐ์ฌ๋ ๊ฐ์ ๊ณ์ฐํ ์ ์๊ฒ ๋๋ค. ์ด ๋ ๊ณ์ฐ๋ ์กฐ๊ฑด๋ถ ๊ธฐ์ฌ๋ ๊ฐ์ ํฌ๊ธฐ์, ์ด์ ๋ฐ์ ์ดํ ๊ฐ ๋ณ์์ ์กฐ๊ฑด๋ถ ๊ธฐ์ฌ๋ ๊ฐ์ ๋ณํ ๋ฐ์ ์๋๋ฅผ ์ข
ํฉ์ ์ผ๋ก ํ๋จํ์ฌ, ์ด์์ ์์ธ ๋ณ์์ ๋ํ ๊ฒฉ๋ฆฌ์ ์ด์ ์ ํ ๊ฒฝ๋ก ๋ถ์์ ํจ๊ณผ์ ์ผ๋ก ์ํํ ์ ์๊ฒ ๋๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ์ ์๋ ์ด์ ๊ฐ์ง ๋ฐ ์ง๋จ ๋ฐฉ๋ฒ๋ก ์ ์ฑ๋ฅ์ ๊ฒ์ฆํ๊ธฐ ์ํ์ฌ ํ
๋ค์ ์ด์คํธ๋ง ๊ณต์ ๋ชจ๋ธ์ ์ด๋ฅผ ์ ์ฉํ๊ณ ๊ฒฐ๊ณผ๋ฅผ ๋ถ์ํ์๋ค. ํ
๋ค์ ์ด์คํธ๋ง ๊ณต์ ์ ์๋
๊ฐ ๊ณต์ ๊ฐ์ ๋ฐฉ๋ฒ๋ก ์ ๊ฒ์ฆํ๊ธฐ ์ํ ๋ฒค์น๋งํฌ ๊ณต์ ์ผ๋ก ๋๋ฆฌ ์ฌ์ฉ๋์ด ์๊ธฐ ๋๋ฌธ์, ์ ์๋ ๋ฐฉ๋ฒ๋ก ์ ์ด์ ์ ์ฉํด ๋ด์ผ๋ก์จ ๋ค๋ฅธ ๊ณต์ ๊ฐ์ ๋ฐฉ๋ฒ๋ก ๋ค๊ณผ์ ์ฑ๋ฅ์ ๋น๊ตํด ๋ณผ ์ ์์๋ค. ๋ํ ๋ค์์ ๋จ์ ๊ณต์ ์ ํฌํจํ๊ณ ์๊ณ , ์ํ์ ์ธ ๋ณ์ ๊ด๊ณ ์ญ์ ํฌํจํ๊ณ ์๊ธฐ ๋๋ฌธ์ ์ ์๋ ๋ฐฉ๋ฒ๋ก ์ ์ฑ๋ฅ์ ์ํํด ๋ณด๊ธฐ์ ์ ํฉํ๋ค. ํ
๋ค์ ์ด์คํธ๋ง ๊ณต์ ๋ด๋ถ์๋ 28๊ฐ ์ข
๋ฅ์ ์ด์์ด ํ๋ก๊ทธ๋จ ์์ ๋ด์ฅ๋์ด ์๋๋ฐ, ์ ์๋ ๊ณต์ ๊ฐ์ง ๋ฐฉ๋ฒ๋ก ์ ์ ์ฉํ ๊ฒฐ๊ณผ ๋ชจ๋ ์ด์์ ๋ํ์ฌ 96% ์ด์์ ๋์ ์ด์ ๊ฐ์ง์จ์ ๋ํ๋ด์๋ค. ์ด๋ ๊ธฐ์กด์ ์ ์๋ ๊ณต์ ๊ฐ์ ๋ฐฉ๋ฒ๋ก ๋ค์ ๋นํ์ฌ ์๋ฑํ ๋์ ์์น์๋ค. ๋ํ ์ด์ ์ง๋จ ์ฑ๋ฅ์ ๋ถ์ํด ๋ณธ ๊ฒฐ๊ณผ, ๋ชจ๋ ์ด์์ ๋ํ์ฌ ์์ธ์ด ๋๋ ๋
ธ๋๋ฅผ ํจ๊ณผ์ ์ผ๋ก ํ์
ํ ์ ์์๊ณ , ์ด์ ์ ํ ๊ฒฝ๋ก ์ญ์ ์ ํํ๊ฒ ํ์งํ์ฌ ๊ธฐ์กด ๋ฐฉ๋ฒ๋ก ๋ค๊ณผ๋ ์ฐจ๋ณํ๋ ์ฑ๋ฅ์ ๋ํ๋ด์๋ค. ์ ์๋ ๋ฐฉ๋ฒ๋ก ์ ํ
๋ค์ ์ด์คํธ๋ง ๊ณต์ ์ ์ ์ฉํด ๋ด์ผ๋ก์จ, ๋ณธ ์ฐ๊ตฌ ๋ด์ฉ์ด ๊ณต์ ์ด์์ ๊ฐ์ง ๋ฐ ์ง๋จ์ ๋ํ ํตํฉ์ ์ธ ๋ฐฉ๋ฒ๋ก ์ค์์ ๊ฐ์ฅ ์ฐ์ํ ์ฑ๋ฅ์ ๋ํ๋ด๋ ๊ฒ์ ํ์ธํ ์ ์์๋ค.Contents
Abstract i
Contents iv
List of Tables vii
List of Figures ix
1 Introduction 1
1.1 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 Outline of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Markov Random Fields Modelling, Graphical Lasso, and Optimal Structure Learning 10
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Markov Random Fields . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 Graphical Lasso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4 MRF Modelling & Structure Learning . . . . . . . . . . . . . . . . . 19
2.4.1 MRF modelling in process systems . . . . . . . . . . . . . . 19
2.4.2 Structure learning using iterative graphical lasso . . . . . . . 20
2.5 Application of Iterative Graphical Lasso on the TEP . . . . . . . . . . 24
3 Efficient Process Monitoring via the Integrated Use of Markov Random
Fields Learning and the Graphical Lasso 31
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2 MRF Monitoring Integrated with Graphical Lasso . . . . . . . . . . . 35
3.2.1 Step 1: Iterative graphical lasso . . . . . . . . . . . . . . . . 36
3.2.2 Step 2: MRF monitoring . . . . . . . . . . . . . . . . . . . . 36
3.3 Implementation of Glasso-MRF monitoring to the Tennessee Eastman
process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.3.1 Tennessee Eastman process . . . . . . . . . . . . . . . . . . 41
3.3.2 Glasso-MRF monitoring on TEP . . . . . . . . . . . . . . . . 48
3.3.3 Fault detection accuracy comparison with other monitoring
techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
3.3.4 Fault detection speed & fault propagation . . . . . . . . . . . 95
4 Process Fault Diagnosis via Markov Random Fields Learning and Inference 101
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.2.1 Probabilistic graphical models & Markov random fields . . . 106
4.2.2 Kernel belief propagation . . . . . . . . . . . . . . . . . . . . 107
4.3 Fault Diagnosis via MRF Modeling . . . . . . . . . . . . . . . . . . 113
4.3.1 MRF structure learning via graphical lasso . . . . . . . . . . 116
4.3.2 Kernel belief propagation - bandwidth selection . . . . . . . . 116
4.3.3 Conditional contribution evaluation . . . . . . . . . . . . . . 117
4.4 Application Results & Discussion . . . . . . . . . . . . . . . . . . . 118
4.4.1 Two tank process . . . . . . . . . . . . . . . . . . . . . . . . 119
4.4.2 Tennessee Eastman process . . . . . . . . . . . . . . . . . . 137
5 Concluding Remarks 152
Bibliography 157
Nomenclature 169
Abstract (In Korean) 170Docto
Probabilistic Graphical Models in RapidMiner
This Report describes the technical background and usage of the GraphMod plug-in for RapidMiner. The plug-in enables RapidMiner to load factor graphs and interpret Label and Attributes which are contained in an Example as assignments to random variables. A set of examples which belong to the same Batch is treated as assignment to a whole factor graph. New operators allow the estimation of factor weights, the computation of the single-node marginal probability functions and the computation of the most probable assignment for each Labelnode with several methods. All algorithms are optimized for parallel execution on common multi-core processors and NVIDIA CUDA capable many-core processors (also known as Graphics Processing Unit)
Introductory Chapter: Prognostics - An Overview
Prognostics, in general, can be defined as โknowledge beforehandโ. Prognostics
is usually identified with medical issues. Nowadays, due to the new advances in
technologies and information systems, prognostic is beginning to be employed in
other fields, e.g. engineering, financial, business, et
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
We describe a method to perform functional operations on probability
distributions of random variables. The method uses reproducing kernel Hilbert
space representations of probability distributions, and it is applicable to all
operations which can be applied to points drawn from the respective
distributions. We refer to our approach as {\em kernel probabilistic
programming}. We illustrate it on synthetic data, and show how it can be used
for nonparametric structural equation models, with an application to causal
inference
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages
We propose an efficient nonparametric strategy for learning a message
operator in expectation propagation (EP), which takes as input the set of
incoming messages to a factor node, and produces an outgoing message as output.
This learned operator replaces the multivariate integral required in classical
EP, which may not have an analytic expression. We use kernel-based regression,
which is trained on a set of probability distributions representing the
incoming messages, and the associated outgoing messages. The kernel approach
has two main advantages: first, it is fast, as it is implemented using a novel
two-layer random feature representation of the input message distributions;
second, it has principled uncertainty estimates, and can be cheaply updated
online, meaning it can request and incorporate new training data when it
encounters inputs on which it is uncertain. In experiments, our approach is
able to solve learning problems where a single message operator is required for
multiple, substantially different data sets (logistic regression for a variety
of classification problems), where it is essential to accurately assess
uncertainty and to efficiently and robustly update the message operator.Comment: accepted to UAI 2015. Correct typos. Add more content to the
appendix. Main results unchange
Nonparametric Detection of Geometric Structures over Networks
Nonparametric detection of existence of an anomalous structure over a network
is investigated. Nodes corresponding to the anomalous structure (if one exists)
receive samples generated by a distribution q, which is different from a
distribution p generating samples for other nodes. If an anomalous structure
does not exist, all nodes receive samples generated by p. It is assumed that
the distributions p and q are arbitrary and unknown. The goal is to design
statistically consistent tests with probability of errors converging to zero as
the network size becomes asymptotically large. Kernel-based tests are proposed
based on maximum mean discrepancy that measures the distance between mean
embeddings of distributions into a reproducing kernel Hilbert space. Detection
of an anomalous interval over a line network is first studied. Sufficient
conditions on minimum and maximum sizes of candidate anomalous intervals are
characterized in order to guarantee the proposed test to be consistent. It is
also shown that certain necessary conditions must hold to guarantee any test to
be universally consistent. Comparison of sufficient and necessary conditions
yields that the proposed test is order-level optimal and nearly optimal
respectively in terms of minimum and maximum sizes of candidate anomalous
intervals. Generalization of the results to other networks is further
developed. Numerical results are provided to demonstrate the performance of the
proposed tests.Comment: Submitted for journal publication in November 2015. arXiv admin note:
text overlap with arXiv:1404.029