基于ImOS-ELM的航空发动机传感器故障自适应诊断技术

doi:10.7527/S1000-6893.2013.0222

Abstract

Abstract:

On-line sequential extreme learning machine (OS-ELM) algorithm is likely to fall into matrix singularity and ill-posedness, and it has no predictive ability at the beginning stage of training. Hence, in this paper, an improved scheme with selection strategy, named improved OS-ELM (ImOS-ELM) algorithm, is proposed. This algorithm overcomes matrix singularity and ill-posedness by regularization, which can improve the predicting accuracy and achieve predictive ability from the start stage of training. Meantime, the output layer weight vector is updated selectively based on generalization capability, and it reduces considerably the mean training time of the algorithm. In order to verify the effectiveness of the proposed algorithm, simulation tests are carried out using time series data, which show that the proposed algorithm achieves higher accuracy and faster speed. Finally, ImOS-ELM algorithm is applied to the sensor fault detection and isolation of an aero-engine. The simulation results show that the sensor faults diagnosis method using the proposed algorithm is able to detect and isolate faults of double-sensor failures and single-sensor drift, which also proves the validity and feasibility of the proposed algorithm.

Key words: extreme learning machine, regularization, selection strategy, aero-engine, sensor, fault detection and isolation

CLC Number:

V233.7

LI Yebo, LI Qiuhong, WANG Jiankang, HUANG Xianghua, ZHAO Yongping. Sensor Fault Adaptive Diagnosis of Aero-engines Based on ImOS-ELM[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(10): 2316-2324.

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Sensor Fault Adaptive Diagnosis of Aero-engines Based on ImOS-ELM

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