Name
Title | ||
|---|---|---|
A Novel Fault Detection with Minimizing the Noise-Signal Ratio Using Reinforcement Learning. |
Abstract | ||
|---|---|---|
In this paper, a reinforcement learning approach is proposed to detect unexpected faults, where the noise-signal ratio of the data series is minimized to achieve robustness. Based on the information of fault free data series, fault detection is promptly implemented by comparing with the model forecast and real-time process. The fault severity degrees are also discussed by measuring the distance between the healthy parameters and faulty parameters. The effectiveness of the algorithm is demonstrated by an example of a DC-motor system. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18093087 | SENSORS |
Keywords | Field | DocType |
fault detection,reinforcement learning,noise-signal ratio | Noise (signal processing),Fault detection and isolation,Fault free,Algorithm,Robustness (computer science),Electronic engineering,Data series,Engineering,Fault severity,Reinforcement learning | Journal |
Volume | Issue | Citations |
18 | 9.0 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dapeng Zhang | 1 | 3 | 3.41 |
| Zhiling Lin | 2 | 3 | 2.06 |
| Zhiwei Gao | 3 | 796 | 61.68 |