Ultrathin hierarchical hydrogel-carbon nanocomposite for highly stretchable fast-response water-proof wearable humidity sensors.

Wearable humidity sensors play an important role in human health monitoring. However, challenges persist in realizing high performance wearable humidity sensors with fast response and good stretchability and durability. Here we report wearable humidity sensors employing an ultrathin micro-nano hierarchical hydrogel-carbon nanocomposite. The nanocomposite is synthesized on polydimethylsiloxane (PDMS) films via a facile two-step solvent-free approach, which creates a hierarchical architecture consisting of periodic microscale wrinkles and vapor-deposited nanoporous hydrogel-candle-soot nanocoating. The hierarchical surface topography results in a significantly enlarged specific surface area (>107 times that of planar hydrogel), which along with the ultrathin hydrogel endow the sensor with high sensitivity and a fast response/recovery (13/0.48 s) over a wide humidity range (11-96%). Owing to the wrinkle structure and interpenetrating network between the hydrogel and PDMS, the sensor is stable and durable against repeated 180° bending, 100% strain, and even scratching. Furthermore, encapsulation of the sensor imparts excellent resistance to water, sweat, and bacteria without influencing its performance. The sensor is then successfully used to monitor different human respiratory behaviors and skin humidity in real time. The reported method is convenient and cost-effective, which could bring exciting new opportunities in the fabrication of next-generation wearable humidity sensors.

A Novel Fault Feature Recognition Method for Time-Varying Signals and Its Application to Planetary Gearbox Fault Diagnosis under Variable Speed Conditions.

The existing time-frequency analysis (TFA) methods mainly highlight the time-frequency ridges of the interested components by optimizing the time-frequency plane to facilitate the extraction of the relevant components. Generalized demodulation (GD), order tracking (OT), and other methods are generally used in conjunction with the TFA methods to realize the transition from a time-varying signal to a stationary signal, and finally identify the fault feature through a time-frequency plane. Generally, it is necessary to clarify the accuracy of the estimated components such as the rotational frequency or the fault characteristic frequency (FCF) during the operation of the GD or OT methods. Unfortunately, it is not only difficult to extract and locate rotational frequency or FCF, but also complicated in the whole estimation process. In this paper, a simple yet readable method is proposed to reveal the fault feature of time-varying signals. First, the method only needs to extract an arbitrary instantaneous frequency (IF). This is different from the GD method which needs to estimate and locate all phase functions. Then, it converts all variable frequency curves into corresponding lines parallel to the frequency axis based on the extracted IF to determine the proportional relationship between the components. Finally, to further improve the readability of the final results, we reduce the dimension of the transformed time-frequency representation to generate a two-dimensional (2D) energy-frequency map with high resolution and the same proportion. Subsequently, the performance is validated by simulated and experimental data.