Early fault diagnosis of bearings is the basis of condition-based maintenance. To overcome the difficulty of early fault diagnosis for the mechanical system, a new conception named quantile multiscale permutation entropy (QMPE) is defined, and a new feature extraction method based on QMPE is proposed. On the basis of the multiscale entropy, the multiscale permutation entropy for the gathered vibration signal of equipment is obtained, and the sample quantile is calculated, which is employed to analyze the weak change of the variation signal. The proposed method is verified with the full lifetime datasets of a certain bearing, which proves that signal features extracted by the QMPE method can not only truly express the bearing detailed condition changing from normal to fault but also duly detect the early fault of the bearing. Comparing with other methods for early fault diagnosis, the proposed method can advance the finding time of the early fault obviously.
The Flight Data Recording System (FDRS) records a lot of parameters of the aircraft during flight, which can be used for the test-flying, training mission of aircraft and so on. Effected by the working environment, information interference and its non-stability, the outliers and noise often exists in the FDRS data. These noises and outliers have a great impact on the use of FDRS. The aim of this paper is to remove outliers and de-noising of navigation data in FDRS. The causes of outliers and noise in FDRS data are analyzed firstly, with a reference suggestion proposed. Then the Letts criterion is used to remove outliers and the Modified Ensemble Empirical Mode Decomposition (MEEMD) is applied to achieve denoising for FDRS. Results demonstrate that outliers are removed and the navigation data are de-noised effectively.
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