Eylem Seç
Enhanced System Health Assessment using Adaptive Self-Learning Techniques
Başlık:
Enhanced System Health Assessment using Adaptive Self-Learning Techniques
Yazar:
Di, Yuan, author.
ISBN:
9780438096561
Yazar Ek Girişi:
Fiziksel Tanımlama:
1 electronic resource (142 pages)
Genel Not:
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Committee members: Thomas Richard Huston; Jay Kim; Manish Kumar; Jay Lee.
Özet:
System health assessment, as one of the most critical tasks in industrial data analytics, focuses on determining the current health condition and detecting the incipient fault. Recently, it has been challenging that the conventional strategy, which relies on a static health reference model along with a fixed threshold, is asked to fulfill the assessment requirements in the nonstationary monitoring environment. The dynamic data contexts might bring incorrect health estimation to the system. This dissertation presents an enhanced systematic online health assessment approach with adaptive self-learning techniques. The method enables the identification of novel working condition states, such as new rotating speed or processing recipe, and the recognition of new degradation extent in the arriving monitoring data, and includes them into the prior learning models. Hence, such continuously growing model could achieve the assessment more efficiently and accurately.
This research work proposes the methodology of the enhanced health assessment approach, along with detailed technologies utilized in each implementation step, including a self-learning technique, a change detection and recognition strategy, and a clustering algorithm. Through a toy case on a rotor test bed, the dissertation intuitively described the detailed assessment process and demonstrated that the proposed approach, compared with the static model solution, could successfully capture the newly encountered patterns in the testing data.
The feasibility of the proposed approach was demonstrated by two industrial use cases. For the semiconductor manufacturing process monitoring case, the proposed approach was able to correctly estimate the health states of the data measured from different experiments while being trained by one experiment observations. Additionally, it surpassed two existed assessment methods with higher overall assessment accuracy. For the power electronics modules monitoring case, the proposed approach also demonstrated its capabilities estimating the components' health states under the situation of dynamic control modes and various units. It outperformed benchmarked unsupervised assessment methods and even obtained competitive results compared with supervised learning solutions.
This study presents that the proposed approach with adaptive self-learning techniques could embrace a wide range of applications when the system health assessment is employed in an inconsistent and dynamic monitoring environment. Looking forward, more investigations would be contributed considering more complicated monitoring situations such as continuous working condition variations and incipient fault detection with multiple failure modes. Furthermore, the proposed approach provides a library of degradation patterns under various working conditions so that it can be potentially used to estimate the system remaining useful life.
Notlar:
School code: 0045
Konu Başlığı:
Tüzel Kişi Ek Girişi:
Mevcut:*
Yer Numarası | Demirbaş Numarası | Shelf Location | Lokasyon / Statüsü / İade Tarihi |
---|---|---|---|
XX(696346.1) | 696346-1001 | Proquest E-Tez Koleksiyonu | Arıyor... |
On Order
Liste seç
Bunu varsayılan liste yap.
Öğeler başarıyla eklendi
Öğeler eklenirken hata oldu. Lütfen tekrar deneyiniz.
:
Select An Item
Data usage warning: You will receive one text message for each title you selected.
Standard text messaging rates apply.