Multi-sensor monitoring is prevalent in modern structural health monitoring (SHM) practice. As the number of sensors and sampling requirements increase, a monitoring sensor network can generate substantial data which are high-volume and high-dimensional, especially for large structure and machinery. In condition-based monitoring (CBM) of rotating machines, e.g., gas turbine, rotor fault diagnosis serves a significant role in the system reliability, safety, and efficiency, which helps reduce potential damage to the on-rotor structures and avoid catastrophic failures. For multi-channel vibration measurement, classical rotor diagnosis approaches typically involve data fusion techniques based on cross spectral analysis for identifying spectral correlation, e.g., cross power spectral density, and/or matrix analysis tool for dimensionality reduction, e.g., principal component analysis. The operation on vectors or matrices may limit their effectiveness for higher-order array or tensor data. To circumvent this limitation, the third order vibrational spectral tensor is generated by representing the multi-channel acceleration spectra as the three-way array. Through nonnegative Tucker decomposition (NTD), the spectral tensor is decomposed into multiple principal factors: the spectral factor, segmental factor, channel-wise factor, and the dense tensor core. The correlations across the characteristic spectral contents and the sensor channels are revealed by the factorization, which enables the diagnosis of rotor fault and facilitates fault localization by identifying the dominant channels of the characteristic spectral factor. The method is validated on an experimental rotor testbed where the faulty channel with crack, rub, or misalignment fault, is effectively localized via 4-channel vibration measurements, which presents a promising approach for multi-sensor fusion and fault diagnosis in rotating machinery health monitoring.
For the depth data of a single time of flight (TOF) sensor, the existing detection can determine the safe distance and issue a safety alarm, but it cannot be combined with the robot attitude. Based on this, a TOF sensor based Principal component analysis (PCA) normal vector estimation method is proposed in this paper, which transforms the depth data into a three-dimensional point cloud, and uses the PCA method to calculate the normal vector of the point cloud. It lays a foundation for real-time attitude adjustment of future robots.
The creep damage is discussed within Rice thermodynamic theory with internal state variable (ISV). A viscoelastic-viscoplastic model with damage is derived by giving the complementary energy density function and kinetic equations of ISVs. The viscoelastic equation covers classical component model, and three creep phases with hardening and damage effect can be described by this model. The model parameter cabibration is conducted through uniaxial creep test of analogue material by loading and unloading method. Then intrinsic thermodynamic properties in three creep stages are indicated. The thermodynamic state of material system tends to equilibrate without damage and depart from equilibrate with damage.
Wind turbines (WT) are increasingly deployed worldwide to harvest wind power from nature, and WT blades are the most crucial components among the WT systems. WT blades are subject to non-stationary time-varying loads and the load information is usually unknown or hard to obtain. This poses great challenges to blade condition monitoring and fault detection. To avoid WT malfunctions and further economic loss, Transmissibility Functions (TF) based approaches have been developed with the purpose of precisely detecting the incipient WT blades defects. In this paper, a recently proposed Wavelet Energy TF (WETF) method which has been successfully applied to WT bearings is transferred to WT blades fault detection. This technique can remove the impacts of external varying loads, requires no excitation information, and demonstrates robustness to noise. The effectiveness of the WETF method for WT blade fault detection is validated on three naturally-damaged industrial-scale WT blades, and its superiority over the conventional Fourier TF (FTF) method is also demonstrated.
Combining the mechanism of the hash chain with the forward-secure proxy blind signature, we firstly propose a new strong forward-secure proxy blind signature scheme. This scheme has many secure characters, such as strong forward security, namely, although the attacker get the key in some period , the attacker cannot forge the signature in the past and future periods, verifiability, unforgeability, distinguishability, identifiability, strong blindness.
Rolling bearing performance degradation assessment has been receiving much attention for which itscrucial role to realize CBM(condition-based maintenance).This paper proposed a novel bearing performance degradation method based on TESPAR(Time Encoded Signal Processing and Recognition)and GMM(Gauss Mixture Model). TESPAR is used to extracted features which constitute A-matrix. GMM is utilized to approximate the density distribution of singular values decomposed by A-matrix. TENLLP(Time-Encoded Negative Log Likelihood Probability) serves as a fault severity which can display the similarity of the singular values between normal samples and fault samples as quantificational. Results of its application to bearing fatigue test show that this performance degradation assessment can detect the incipient rolling bearing fault and be sensitive to the change of fault.
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