Data-driven fault diagnosis for heterogeneous chillers using domain adaptation techniques

Sand, Ron van de; Corasaniti, Sandra; Reiff-Stephan, Jörg

doi:10.1016/j.conengprac.2021.104815

Cited by 25 publications

(3 citation statements)

References 46 publications

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“…This approach was validated on bearing condition diagnosis. van de Sand et al [199] realized conditional alignment by adapting the decision boundaries of the classifier to the target domain. First, a classifier was trained by the TCA-transformed labeled source data; then, some of the labeled source data were replaced by pseudo labeled target data, and the training of the classifier was continued.…”

Section: ) Transfer Component Analysis and Joint Distribution Adaptationmentioning

confidence: 99%

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Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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Prognostics and health management (PHM) is an engineering approach dealing with the diagnosis, prognosis, and management of the health state of engineering systems. Methods that can analyze system behavior, fault conditions, and degradation are crucial for PHM applications, as they create the basis for determining, predicting, and monitoring the health of engineering systems. Data-driven methods have been proven to be suitable for automated diagnosis or prognosis due to their pattern recognition and anomaly detection abilities. Moreover, they do not require knowledge of the underlying degradation process. However, training data-driven methods usually requires a large amount of data, whose collection, cleansing, organization, and preparation are generally very time-consuming and costly. There are usually little or no run-to-failure data available at market launch, especially for new systems such as new machine generations. Nevertheless, related systems, hereinafter referred to as similar systems, often already exist, differing only in some technical characteristics. In this paper, the similar system problem is defined, and explanations of the difficulties that arise when using data from similar systems are presented. Furthermore, it is discussed why the usage of these data offers great potential for condition diagnosis and prognosis of engineering systems. An overview of data-driven methods that can be used to utilize data from similar systems is provided, and the methods that such systems already consider are highlighted. Two related research areas are identified, namely, fleet learning and transfer learning. In the paper, it is shown that similar system approaches will become an important branch of research in PHM. However, some difficulties must be overcome.INDEX TERMS Condition diagnosis, condition prognosis, data-driven methods, fleet learning, prognostics and health management (PHM), similar system approach, similar system problem, transfer learning.

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Section: ) Transfer Component Analysis and Joint Distribution Adaptationmentioning

confidence: 99%

“…In [253], building chillers with different cooling capacities, powers, and structures were considered. van de Sand et al [199], Fan et al [159], and Li et al [293] also transferred knowledge between two different chiller types. However, the latter focuses on energy optimization rather than condition diagnosis or prognosis.…”

Section: E Other Similar Systemsmentioning

confidence: 99%

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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“…The main reason is the availability of public open-source fault data of bearings and gearboxes gathered from different machines in different working operations, which makes cross-domain studies easier to be executed. Only a few cross-domain PdM preliminary studies cope with different kinds of components or machines, such as Tennessee-Eastman (TE) process[104], Turbofan Engine[106], Chillers[80], Induction Motor[154], 3D Printer[32], Power Transmission Line Inspection[160], Fed-batch Penicillin Fermentation[182].…”

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confidence: 99%

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

et al. 2023

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The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.

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A Review of Data-Driven Machinery Fault Diagnosis Using Machine Learning Algorithms

Cen

Yang

Liu

et al. 2022

J. Vib. Eng. Technol.

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Data-driven fault diagnosis for heterogeneous chillers using domain adaptation techniques

Cited by 25 publications

References 46 publications

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

A Review of Data-Driven Machinery Fault Diagnosis Using Machine Learning Algorithms

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