Intrinsic component filtering for fault diagnosis of rotating machinery

Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of column features and l3=2-norm of row features,is proposed for the machinery fault diagnosis.ICF can be used as a feature learning algorithm,and the learned features can be fed into the classification to achieve the automatic fault classification.ICF can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior experience.Simulated and experimental signals of bearing fault are used to validate the performance of ICF.The results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis,weak signature detection and compound fault separation.

Parametric identification of time-varying systems from free vibration using intrinsic chirp component decomposition

Time-varying systems are applied extensively in practical applications,and their related parameter identification techniques are of great significance for structural health monitoring of time-varying systems.To improve the identification accuracy for time-varying systems,this study puts forward a novel parameter identification approach in the time-frequency domain using intrinsic chirp component decomposition(ICCD).ICCD is a powerful tool for signal decomposition and parameter extraction,with good signal reconstruction capability in a high-noise environment.To maintain good identification effects for the time-varying system in a noisy environment,the proposed method introduces a redundant Fourier model for the non-stationary signal,including instantaneous frequency(IF)and instantaneous amplitude(IA).The accuracy and effectiveness of the proposed approach are demonstrated by a single-degree-of-freedom system with three types of time-varying parameters,as well as an example of a multi-degree-of-freedom system.The effects of different levels of measured noise on the identified results are also discussed in detail.Numerical results show that the proposed method is very effective in tracking the smooth,periodical,and non-smooth variations of time-varying systems over the entire identification time period even when the response signal is contaminated by intense noise.

Transparent, stretchable, and rapid-response humidity sensor for body-attachable wearable electronics

Stretchable and conformal humidity sensors that can be attached to the human body for continuously monitoring the humidity of the environment around the human body or the moisture level of the human skin can play an important role in electronic skin and personal healthcare applications. However, most stretchable humidity sensors are based on the geometric engineering of non-stretchable components and only a few detailed studies are available on stretchable humidity sensors under applied mechanical deformations. In this paper, we propose a transparent, stretchable humidity sensor with a simple fabrication process, having intrinsically stretchable components that provide high stretchability, sensitivity, and stability along with fast response and relaxation time. Composed of reduced graphene oxide-polyurethane composites and an elastomeric conductive electrode, this device exhibits impressive response and relaxation time as fast as 3.5 and 7 s, respectively. The responsivity and the response and relaxation time of the device in the presence of humidity remain almost unchanged under stretching up to a strain of 60% and after 10,000 stretching cycles at a 40% strain. Further, these stretchable humidity sensors can be easily and conformally attached to a finger for monitoring the humidity levels of the environment around the human body, wet objects, or human skin.