A novel manifold–manifold distance index applied to looseness state assessment of viscoelastic sandwich structures

Abstract

Viscoelastic sandwich structures (VSS) are widely used in mechanical equipment; their state assessment is necessary to detect structural states and to keep equipment running with high reliability. This paper proposes a novel manifold-manifold distance-based assessment (M 2 DBA) method for assessing the looseness state in VSSs. In the M 2 DBA method, a manifold-manifold distance is viewed as a health index. To design the index, response signals from the structure are firstly acquired by condition monitoring technology and a Hankel matrix is constructed by using the response signals to describe state patterns of the VSS. Thereafter, a subspace analysis method, that is, principal component analysis (PCA), is performed to extract the condition subspace hidden in the Hankel matrix. From the subspace, pattern changes in dynamic structural properties are characterized. Further, a Grassmann manifold (GM) is formed by organizing a set of subspaces. The manifold is mapped to a reproducing kernel Hilbert space (RKHS), where support vector data description (SVDD) is used to model the manifold as a hypersphere. Finally, a health index is defined as the cosine of the angle between the hypersphere centers corresponding to the structural baseline state and the looseness state. The defined health index contains similarity information existing in the two structural states, so structural looseness states can be effectively identified. Moreover, the health index is derived by analysis of the global properties of subspace sets, which is different from traditional subspace analysis methods. The effectiveness of the health index for state assessment is validated by test data collected from a VSS subjected to different degrees of looseness. The results show that the health index is a very effective metric for detecting the occurrence and extension of structural looseness. Comparison results indicate that the defined index outperforms some existing state-of-the-art ones.

Looseness condition feature extraction of viscoelastic sandwich structure using dual-tree complex wavelet packet-based deep autoencoder network

“…The major drawback of this method is that a great deal of training signals are needed, which significantly restricts the practical application of the method. Sun et al 7 assessed the looseness state of VSS by constructing the manifold–manifold distance index based on the vibration response signals. In this method, the assessment index is constructed based on the signals from six sensors, which increased the complexity of the method in practical application.…”

Generalized variational mode decomposition for interlayer slipping detection of viscoelastic sandwich cylindrical structures

“…A viscoelastic sandwich cylindrical structure (VSCS) typically consists of a base layer, a sandwich layer, and a constraining layer, which remains stable by pre-tightening force and interface friction [1,2]. Due to its viscoelastic properties, the sandwich material can suppress vibration, reduce noise, seal and compensate for the gap changes caused by thermal expansion and contraction.…”

Quantitative Index and Abnormal Alarm Strategy Using Sensor-Dependent Vibration Data for Blade Crack Identification in Centrifugal Booster Fans

“…For a rotor system, the stiffness of the shaft would display cyclical behavior once blade cracks occur [ 4 ], and the dynamic response signal will show obvious non-stationary characteristics. Fault diagnosis for blade crack faults is a difficult problem that has drawn the attention of top scholars and outstanding engineers worldwide [ 5 , 6 , 7 , 8 ]. Lots of related studies have been reported in important journals [ 9 , 10 , 11 ].…”