Unsupervised Fault Detection for Building Air Handling Unit Systems Using Deep Variational Mixture of Principal Component Analyzers
Why this work is in the frame
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Bibliographic record
Abstract
Incomplete data is the most common but tricky problem for data-driven energy and building solutions. Due to sensor errors or communication failures, raw building data with missing data points are rarely satisfactory for fault detection and diagnosis (FDD) applications. In this paper, a new framework named a deep variational mixture of principal component analyzers (DV-MPPCA) is proposed to address the building FDD problem with incomplete data. DV-MPPCA is the combination of a variational autoencoder (VAE) model for data compression and a mixture of principal component analyzers (MPPCA) for density estimation. To construct an integrated framework comprising both VAE and MPPCA, we introduce a novel methodology that represents the algebraic model of MPPCA within the architecture of a neural network. This innovative architecture undergoes optimization through the minimization of a designated loss function. Subsequently, the refined and optimized framework is harnessed as an unsupervised fault detection model for a real-world air handling unit (AHU) system designed by the ASHRAE research project 1312 (RP-1312). Furthermore, by incorporating the modified evidence lower bound (ELBO) loss function within the VAE, the resulting DV-MPPCA framework exhibits exceptional performance when confronted with incomplete AHU datasets, even with high missing rates. Empirical findings substantiate the supremacy of DV-MPPCA over other contemporary classic and deep models. Impressively, even with a missing rate as modest as 10%, DV-MPPCA consistently delivers outstanding performance, achieving F1-scores of 98.10%, 93.50%, and 81.57% for the Summer, Winter, and Spring datasets, respectively. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the fact that raw building data with missing data points are not qualitative enough for direct use in fault detection and diagnosis (FDD) applications. To resolve this problem, existing studies manipulated imputation algorithms in the preprocessing step to prepare the data for constructing FDD models and impute missing points in test instances during the online monitoring process. However, this step makes an overall FDD framework cumbersome. Therefore, instead of utilizing a data imputation method as a separately operating model, we adopt VAE in our framework to exclude the contribution of missing points during the offline modeling and online monitoring processes. This modification of VAE helps our framework be immune to incomplete data. For reproducibility and future improvement by other researchers, the complete source code of this study is provided in the following repository: https://github.com/viettra-xai/DV-MPPCA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it