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An Active Learning Method Based on Uncertainty and Complexity for Gearbox Fault Diagnosis
Beihang University, Beijing, China.
Beihang University, Beijing, China.
Beihang University, Beijing, China.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0002-7458-6820
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2019 (Engelska)Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 9022-9031Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

It is crucial to implement an effective and accurate fault diagnosis of a gearbox for mechanical systems. However, being composed of many mechanical parts, a gearbox has a variety of failure modes resulting in the difficulty of accurate fault diagnosis. Moreover, it is easy to obtain raw vibration signals from real gearbox applications, but it requires significant costs to label them, especially for multi-fault modes. These issues challenge the traditional supervised learning methods of fault diagnosis. To solve these problems, we develop an active learning strategy based on uncertainty and complexity. Therefore, a new diagnostic method for a gearbox is proposed based on the present active learning, empirical mode decomposition-singular value decomposition (EMD-SVD) and random forests (RF). First, the EMD-SVD is used to obtain feature vectors from raw signals. Second, the proposed active learning scheme selects the most valuable unlabeled samples, which are then labeled and added to the training data set. Finally, the RF, trained by the new training data, is employed to recognize the fault modes of a gearbox. Two cases are studied based on experimental gearbox fault diagnostic data, and a supervised learning method, as well as other active learning methods, are compared. The results show that the proposed method outperforms the two common types of methods, thus validating its effectiveness and superiority.

Ort, förlag, år, upplaga, sidor
IEEE, 2019. Vol. 7, s. 9022-9031
Nyckelord [en]
Active learning, gearbox fault diagnosis, uncertainty and complexity, supervised learning
Nationell ämneskategori
Teknik och teknologier Annan samhällsbyggnadsteknik
Forskningsämne
Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-72603DOI: 10.1109/ACCESS.2019.2890979ISI: 000456910200001Scopus ID: 2-s2.0-85060708128OAI: oai:DiVA.org:ltu-72603DiVA, id: diva2:1279765
Anmärkning

Validerad;2019;Nivå 2;2019-01-29 (svasva)

Tillgänglig från: 2019-01-17 Skapad: 2019-01-17 Senast uppdaterad: 2019-02-11Bibliografiskt granskad

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Lin, Jing

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