Optimal Training and Efficient Model Selection for Parameterized Large Margin Learning

Zhou, Yuxun; Baek, Jae Yeon; Li, Dan; Spanos, Costas J.

doi:10.1007/978-3-319-31753-3_5

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…By training SVDD models for each fault type, they extended similar idea to a chiller fault diagnosis strategy in [32]. Noticing that training a one-class classification model for each specific 60 fault type is computationally costly, an alternative method is to formulate the FDD issue directly as a multi-class classification problem. To list a few, Du A c c e p t e d M a n u s c r i p t Compared with previous building FDD works, this paper presents its contributions in several ways.…”

Section: Introductionmentioning

confidence: 98%

Fault detection and diagnosis for building cooling system with a tree-structured learning method

Zhou

et al. 2016

Energy and Buildings

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Section: Introductionmentioning

confidence: 98%

Fault detection and diagnosis for building cooling system with a tree-structured learning method

Zhou

et al. 2016

Energy and Buildings

Self Cite

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“…Driven by the superiority in revealing the underlying patterns and correlations among features over traditional model-based methods, a wide range of supervised pattern classification techniques have been explored in the building FDD field. To list a few, existing literature includes multivariate regression models [5], Bayes classifiers [6], neural networks (NN) [7,8], linear discriminant analysis (LDA) [3], Gaussian mixture models [9], support vector machines (SVM) [10,11], and tree-structured learning method [12,13]. However, supervised data-driven FDD methods have limited usefulness in practice due to the following factors:…”

Section: Introductionmentioning

confidence: 99%

Cross Domain Data Generation for Smart Building Fault Detection and Diagnosis

Zhou

et al. 2022

Mathematics

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Benefiting extensively from the Internet of Things (IoT) and sensor network technologies, the modern smart building achieves thermal comfort. It prevents energy wastage by performing automatic Fault Detection and Diagnosis (FDD) to maintain the good condition of its air-conditioning systems. Often, real-time multi-sensor measurements are collected, and supervised learning algorithms are adopted to exploit the data for an effective FDD. A key issue with the supervised methods is their dependence on well-labeled fault data, which is difficult to obtain in many real-world scenarios despite the abundance of unlabelled sensor data. Intuitively, the problem can be greatly alleviated if some well-labeled fault data collected under a particular setting can be re-used and transferred to other cases where labeled fault data is challenging or costly. Bearing this idea, we proposed a novel Adversarial Cross domain Data Generation (ACDG) framework to impute missing fault data for building fault detection and diagnosis where labeled data is costly. Unlike traditional Transfer Learning (TL)-related applications that adapt models or features learned in the source domain to the target domain, ACDG essentially “generates” the unknown sensor data for the target setting (target domain). This is accomplished by capturing the data patterns and common knowledge from known counterparts in the other setting (source domain), the inter-domain knowledge, and the intra-domain relations. The proposed ACDG framework is tested with the real-world Air Handling Unit (AHU) fault dataset of the ASHRAE Research Project 1312. Extensive experimental results on the cross-domain AHU fault data showed the effectiveness of ACDG in supplementing the data for a missing fault category by exploiting the underlying commonalities between different domain settings.

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“…Compared with the traditional model-based methods [7]- [9], data-driven FDD method is becoming more and more popular due to its superiority in revealing the underlying patterns and relationships [10]- [12]. As a result, a wide range of pattern classification techniques have been explored as data-driven methods in the building FDD field, including multivariate regression models [13], Bayes classifiers [14], neural networks (NNs) [15], linear discriminant analysis (LDA) [16], Gaussian mixture models [17], support vector data description [18], [19], support vector machines [20], [21], and tree-structured learning method [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Identifying Unseen Faults for Smart Buildings by Incorporating Expert Knowledge With Data

Zhou

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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Thanks to the development of sensor networks and information technology, data-driven fault detection and diagnosis (FDD) is getting more and more popular with rich data. In the building FDD field, mature supervised learning algorithms and strategies have been applied to detect and diagnose known faults. However, it is out of the question to collect labeled training data for every possible fault. Thus, there is a necessity to study FDD when the training data for some faults are unavailable. To the authors' best knowledge, few works have reported how to identify "unseen faults." In this paper, authors propose a novel expert knowledge-based unseen fault identification (EK-UFI) method to identify unseen faults by employing the similarities between known faults and unknown faults. The similarity is captured by incorporating essential expert knowledge that is encoded in the fault gene matrix. The fault gene is integrated with a latent incorporation matrix that transfers knowledge from known faults to unseen faults. With application to a real system, the proposed method is proven to be effective in identifying various building unknown faults with a high accuracy. Note to Practitioners-FDD is of great importance for saving energy and improving occupancy comfort levels and building safety levels. Identifying unseen faults in real application is challenging since: 1) building faults are complicated and confusing while well-labeled fault data is rare; 2) experimental fault data collected in laboratory test beds cannot be directly used as judgment criteria for real buildings; and 3) it is impossible to measure every possible fault ahead of time. Although supervised learning methods have been successfully applied in existing works to solve the building FDD, they could not attack the UFI problem. In this paper, a novel EK-UFI method is proposed to identify unseen faults by employing the similarities between known faults and unknown faults. Experimental results show that the proposed method is essential.

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Optimal Training and Efficient Model Selection for Parameterized Large Margin Learning

Cited by 6 publications

References 10 publications

Fault detection and diagnosis for building cooling system with a tree-structured learning method

Fault detection and diagnosis for building cooling system with a tree-structured learning method

Cross Domain Data Generation for Smart Building Fault Detection and Diagnosis

Identifying Unseen Faults for Smart Buildings by Incorporating Expert Knowledge With Data

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