Binqiang Chen

Novel method for bearing performance degradation assessment – A kernel locality preserving projection-based approach:

Bearing performance degradation assessment is meaningful for keeping mechanical reliability and safety. For this purpose, a novel method based on kernel locality preserving projection is proposed in this article. Kernel locality preserving projection extends the traditional locality preserving projection into the non-linear form by using a kernel function and it is more appropriate to explore the non-linear information hidden in the data sets. Considering this point, the kernel locality preserving projection is used to generate a non-linear subspace from the normal bearing data. The test data are then projected onto the subspace to obtain an index for assessing bearing degradation degrees. The degradation index that is expressed in the form of inner product indicates similarity of the normal data and the test data. Validations by using monitoring data from two experiments show the effectiveness of the proposed method.

Manifold subspace distance derived from kernel principal angles and its application to machinery structural damage assessment

Damage assessment of machinery structure is beneficial for identifying structural health states and preventing sudden failures. A novel scheme for damage assessment is presented by using manifold subspace distance in this study. Vibration response signals from the machinery structure are collected by accelerometers first, and feature matrices are extracted to characterize the acceleration response comprehensively. Thereafter, a manifold learning method, namely kernel locality preserving projection (KLPP), is performed to obtain manifold subspace from the feature matrix. KLPP is available to mine nonlinear information hidden in the feature matrix, which makes it different from the linear subspace analysis method. This merit enables KLPP to be more effective in exploring the intrinsic model of the feature matrix. Further, kernel principal angles that represent similarity between the manifold subspaces are calculated. Finally, a manifold subspace distance is derived from the kernel principal angles. This distance is an appropriate metric for measuring closeness or similarity between the subspaces embedded on a manifold. The distance between manifold subspaces from normal state and damage state of a machinery structure is defined as a damage index. Effectiveness of the proposed scheme is validated by two case studies with regard to damage assessment for different machinery structures. The results show that the defined damage index is not only sensitive to the occurrence of structural damage but also increases obviously with the increasing damage level. These positive results illustrate that the proposed scheme has promise for future performance and is a valuable method for damage assessment. (Some figures may appear in colour only in the online journal)

Incipient-signature identification of mechanical anomalies in a ship-borne satellite antenna system using an ensemble multiwavelet

The instrumented tracking and telemetry ship with a ship-borne satellite antenna (SSA) is the critical device to ensure high quality of space exploration work. To effectively detect mechanical anomalies that can lead to unexpected downtime of the SSA, an ensemble multiwavelet (EM) is presented for identifying the anomaly related incipient-signatures within the measured dynamic signals. Rather than using a predetermined basis as in a conventional multiwavelet, an EM optimizes the matching basis which satisfactorily adapts to the anomaly related incipient-signatures. The construction technique of an EM is based on the conjunction of a two-scale similarity transform (TST) and lifting scheme (LS). For the technique above, the TST improves the regularity by increasing the approximation order of multiscaling functions, while subsequently the LS enhances the smoothness and localizability via utilizing the vanishing moment of multiwavelet functions. Moreover, combining the Hilbert transform with EM decomposition, we identify the incipient-signatures induced by the mechanical anomalies from the measured dynamic signals. A numerical simulation and two successful applications of diagnosis cases (a planetary gearbox and a roller bearing) demonstrate that the proposed technique is capable of dealing with the challenging incipient-signature identification task even though spectral complexity, as well as the strong amplitude/frequency modulation effect, is present in the dynamic signals.

Kurtosis-Based Constrained Independent Component Analysis and Its Application on Source Contribution Quantitative Estimation

Aiming at finding the major vibration and noise sources of vehicles, a quantitative estimation method for source contribution using the kurtosis-based constrained independent component analysis (cICA) algorithm is proposed. First, the similarity between the ICs and the reference signals with given characteristics is described by a concise and effective closeness measurement function. Meanwhile, how to choose the reference signals and the choice of some other closeness measurements is discussed. Then, a widely used contrast function, namely, kurtosis, is modified by the closeness measurement to obtain an enhanced contrast function. The fixed-point iteration and deflation approach are employed to train the separating matrix. Then, the enhanced contrast function is therefore maximized and the kurtosis-based cICA algorithm is obtained. After that, the source contribution is quantitatively calculated by the reduced energy of the mixed signals in each extraction: the reduction of the energy in mixed signals corresponds to the contribution of the extracted IC. The correspondence relationship between the ICs and source signals can be obtained by prior knowledge. Finally, the effectiveness of the proposed algorithm is verified by numerical simulation and experiments. The results show that the proposed method has high accuracy in separating sources and quantitatively calculating the source contribution.

An improved independent component analysis by reference signals and its application on source contribution estimation

To estimate the contribution of main vibration and noise sources of vehicles, a source contribution estimation method based on an improved independent component analysis (ICA) algorithm is proposed. A measure of the similarity between the independent components and the reference signals with given characteristics is introduced. A widely used contrast function, namely kurtosis, is enhanced by the measure to obtain an improved contrast function. By means of fixed-point iteration and deflation approach, the improved contrast function is optimized and the improved ICA algorithm is attained. The contribution is computed by the reduced energy in each extraction, the reduction of the energy corresponds to the contribution of the extracted independent components. The correspondence relationship between the independent components (ICs) and source signals can be obtained by the signal characteristics. The effectiveness of the proposed algorithm is verified by numerical simulation and experiment.

Pseudo non-dyadic second generation wavelet tight frame for machine fault diagnosis

This paper investigates a novel wavelet tight frame (WTF) constructing strategy for obtaining pseudo second generation bases with non-dyadic time-frequency partition grids. The proposed constructing strategy starts with a known second generation wavelet basis and applies it in an undecimated filterbank. Via forward and inverse transformation procedure, an input signal is decomposed into a set of dyadic wavelet packets. By introducing a systematic way of wavelet packet reordering and ensemble wavelet generating method, another corresponding set of non-dyadic wavelet packets are generated. It is demonstrated that the derived pseudo non-dyadic second generation wavelet packets (PNSGW) are associated with well-defined band-pass filters, which ensure good time-frequency localizability and exact shift-invariance. The PNSGW strategy is combined with spectral kurtosis to detect incipient faults in mechanical systems. The processing results show that the proposed technique is a well complement to conventional dyadic wavelet transform in investigating transient impulse responses caused by faulty mechanical components.