A Data-Driven Approach for the Diagnosis of Mechanical Systems Using Trained Subtracted Signal Spectrograms
| العنوان: | A Data-Driven Approach for the Diagnosis of Mechanical Systems Using Trained Subtracted Signal Spectrograms |
|---|---|
| المؤلفون: | Huan Pham Van, Hae-Jin Choi, Jiung Huh, Soonyoung Han, Seung-Kyum Choi |
| المصدر: | Sensors, Vol 19, Iss 5, p 1055 (2019) Sensors Volume 19 Issue 5 Sensors (Basel, Switzerland) |
| بيانات النشر: | MDPI AG, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | 0209 industrial biotechnology, Computer science, 02 engineering and technology, lcsh:Chemical technology, Biochemistry, Signal, Article, Analytical Chemistry, Data-driven, 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, lcsh:TP1-1185, non-stationary signal, Electrical and Electronic Engineering, Instrumentation, Signal processing, business.industry, 020208 electrical & electronic engineering, Process (computing), smart factory, prognostics and health management (PHM), Pattern recognition, Atomic and Molecular Physics, and Optics, Time–frequency analysis, wavelet package decomposition (WPD), Mechanical system, data-driven, Spectrogram, critical information map (CIM), Artificial intelligence, business, industrial robot |
| الوصف: | Toward the prognostic and health management of mechanical systems, we propose and validate a novel effective, data-driven fault diagnosis method. In this method, we develop a trained subtracted spectrogram, the so called critical information map (CIM), identifying the difference between the signal spectrograms of normal and abnormal status. We believe this diagnosis process may be implemented in an autonomous manner so that an engineer employs it without expert knowledge in signal processing or mechanical analyses. Firstly, the CIM method applies sequential and autonomous procedures of time-synchronization, time frequency conversion, and spectral subtraction on raw signal. Secondly, the subtracted spectrogram is then trained to be a CIM for a specific mechanical system failure by finding out the optimal parameters and abstracted information of the spectrogram. Finally, the status of a system health can be monitored accurately by comparing the CIM with an acquired signal map in an automated and timely manner. The effectiveness of the proposed method is successfully validated by employing a diagnosis problem of six-degree-of-freedom industrial robot, which is the diagnosis of a non-stationary system with a small amount of training datasets. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1424-8220 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f205f590ecab676446df7d2ccb46523b http://www.mdpi.com/1424-8220/19/5/1055 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....f205f590ecab676446df7d2ccb46523b |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14248220 |
|---|