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Lu C.,Beihang University | Lu C.,Science and Technology Laboratory on Reliability and Environmental Engineering | Lu C.,State Key Laboratory of Virtual Reality Technology and Systems | Cheng Y.,Beihang University | And 3 more authors.
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering | Year: 2014

Control components of the aircraft environmental control system (AECS), which is fast becoming an increasingly complex system, are of significant importance from the viewpoint of safety. However, few studies have focused on fault diagnosis of the AECS. This study proposes a method based on adaptive threshold and parameter extraction (ATPE) to realize fault detection and isolation for control components in the AECS. To overcome the drawback of a fixed threshold for fault detection, a practical approach is employed by combining a radial basis function (RBF)-based observer with an RBF-based adaptive threshold producer. The RBF neural network observer is used to generate a residual error signal. By comparing the residual error signal with the adaptive threshold, fault occurrence can be detected. To improve the fault isolation accuracy, an RBF fault tracker is used; the parameters of this tracker are extracted for fault isolation along with the residual error, unlike in the case of conventional fault diagnosis methods that are based on a single residual error signal. Finally, an RBF-based fault isolator is adopted to realize fault isolation and classification. Two commonly occurring faults in the control components of the AECS are simulated to verify the performance and effectiveness of the proposed method. The experimental results demonstrate that the proposed method based on ATPE is effective for fault detection and isolation for the control components in the AECS. © IMechE 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav. Source

Lu C.,Beihang University | Lu C.,Science and Technology Laboratory on Reliability and Environmental Engineering | Tao L.,Beihang University | Tao L.,Science and Technology Laboratory on Reliability and Environmental Engineering | Fan H.,Aerospace Measurement and Control
Mechanical Systems and Signal Processing | Year: 2014

Numerous techniques and methods have been proposed to reduce the production downtime, spare-part inventory, maintenance cost, and safety hazards of machineries and equipment. Prognostics are regarded as a significant and promising tool for achieving these benefits for machine maintenance. However, prognostic models, particularly probabilistic-based methods, require a large number of failure instances. In practice, engineering assets are rarely being permitted to run to failure. Many studies have reported valuable models and methods that engage in maximizing both truncated and failure data. However, limited studies have focused on cases where only truncated data are available, which is common in machine condition monitoring. Therefore, this study develops an intelligent machine component prognostics system by utilizing only truncated histories. First, the truncated Minimum Quantization Error (MQE) histories were obtained by Self-organizing Map network after feature extraction. The chaos-based parallel multilayer perceptron network and polynomial fitting for residual errors were adopted to generate the predicted MQEs and failure times following the truncation times. The feed-forward neural network (FFNN) was trained with inputs both from the truncated MQE histories and from the predicted MQEs. The target vectors of survival probabilities were estimated by intelligent product limit estimator using the truncation times and generated failure times. After validation, the FFNN was applied to predict the machine component health of individual units. To validate the proposed method, two cases were considered by using the degradation data generated by bearing testing rig. Results demonstrate that the proposed method is a promising intelligent prognostics approach for machine component health. © 2013 Elsevier Ltd .All rights reserved. Source

Lu C.,Beihang University | Lu C.,Science and Technology on Reliability and Environmental Engineering Laboratory | Tao L.,Beihang University | Tao L.,Intelligent Maintenance | And 2 more authors.
Journal of Power Sources | Year: 2014

The majority of methods used for lithium-ion (Li-ion) capacity estimation are usually restricted to certain applications. Such methods often are time consuming and inconsistent with actual experimental data as well as depending on complicated battery operating and/or aging conditions. A geometrical approach to Li-ion battery capacity estimation is presented in this work. The proposed method utilizes four geometrical features that are sensitive to slight changes in the performance degradation of a Li-ion battery. The Laplacian Eigenmap method is used to establish an intrinsic manifold, and the geodesic on the manifold is used to estimate battery capacity. Tests are conducted based on data obtained under different operating and aging conditions provided by NASA Prognostics Center of Excellence. The evaluation results suggest that the proposed geometrical approach can be used to estimate Li-ion battery capacity accurately for the conditions given in this article. © 2014 Elsevier B.V. All rights reserved. Source

Zhu M.,Harbin Institute of Technology | Wang S.-J.,Aerospace Measurement and Control | Yang C.-L.,Harbin Institute of Technology
Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology | Year: 2010

This paper proposed a realization method of BIST technique of digital circuits based on improved Tent chaotic sequence to address the problem of testing digital circuits. Random sequence of ″0-1″ with white noise characteristics which generated by improved tent chaotic logistic map model hardware implementation is used as automatic test pattern generation (ATPG) of digital circuits. Test response signatures of chaotic sequence are obtained from CRC analysis of output response. It is shown that the method presented in this paper is easy for realization of BIST and has superior performance of higher rate of fault detection and fault isolation than that of M sequence. It is suitable for large-scale FPGA and automatic testing of other programmable logic circuits. Source

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