More effective prognostics with elbow point detection and deep learning
2021 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 146, article id 106987Article in journal (Refereed) Published
Abstract [en]
Prior to failure, most systems exhibit signs of changed characteristics. The early detection of this change is important to remaining useful life estimation. To have the ability to detect the inflection point or “elbow point” of an asset, i.e. the point of the degradation curve that marks the transition from nominal to faulty condition, can enable more sophisticated prognostics because this divide and conquer tactic allows the prediction to focus on the window before failure when significant changes are being expected. In this work, we compare prognostics with and without change point detection. We use different recurrent neural network techniques (standard recurrent neural network, long short-term memory and gated recurrent unit) to find the elbow point location. The actual estimation of the remaining time to failure is based on the echo state network, a state-of-the-art approach in prognostics. Two different experiments are performed on simulated data obtained from NASA Ames prognostics repository. We first compare the performance of the elbow point detectors based on recurrent neural networks against three baseline models: the Z-test, multi-layer perceptron and random forests. Results indicate that recurrent neural networks can outperform the baseline approaches. In the second experiment, the best elbow detection model, the gated recurrent unit, is integrated within an echo state network, with a significant increase in overall performance in terms of remaining useful life estimation.
Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 146, article id 106987
Keywords [en]
Reliability, Prognostics, Elbow point detection, Recurrent neural networks, Echo state network, C-MAPSS datasets
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-79719DOI: 10.1016/j.ymssp.2020.106987ISI: 000558741300016Scopus ID: 2-s2.0-85086075089OAI: oai:DiVA.org:ltu-79719DiVA, id: diva2:1442644
Note
Validerad;2020;Nivå 2;2020-06-17 (alebob)
2020-06-172020-06-172021-12-21Bibliographically approved