Leakage fault detection in Electro-Hydraulic Servo Systems using a nonlinear representation learning approach
| Autor: | S. Mehdi Rezaei, Siavash Sharifi, Mohammad Zareinejad, Bijan Mollaei-Dariani, Ali Tivay |
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| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computer science Applied Mathematics 020208 electrical & electronic engineering 02 engineering and technology Servomechanism Fault detection and isolation Computer Science Applications law.invention Nonlinear system 020901 industrial engineering & automation Control and Systems Engineering law Control theory 0202 electrical engineering electronic engineering information engineering Current sensor Electrical and Electronic Engineering Raw data Actuator Instrumentation Feature learning Leakage (electronics) |
| Zdroj: | ISA transactions. 73 |
| ISSN: | 1879-2022 |
| Popis: | Electro-Hydraulic Servo Systems (EHSS) are employed as actuators to track the desired trajectory and exert force in heavy-duty industrial applications. The EHSS is often prone to problems such as leakage and actuator seal damage during the course of its utilization. These faults which cannot be directly detected from current sensor values, can eventually result in complications and degrade control performance. The goal of this research is to use representation learning concepts to detect these faults with decreased complexity. The objective is to find a nonlinear mapping to transform raw data into another space in which classification becomes easier. The data are driven from the hydraulic supply pressure signal. To find the mapping, a custom-built optimization algorithm is proposed along with a suitable cost function to carry out the search for the new representation. The performance of the resulting transformation is tested in an experimental setting to show the merits of the proposed method. |
| Databáze: | OpenAIRE |
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