Flexible pressure sensors have been widely used in wearable devices, medical and health, smart services and other industries. However, the fabrication of sensor with high sensitivity, large sensing range and good stability is still a vital research topic. Herein, a flexible capacitive pressure sensor based on micro-structured electrode is developed, which uses a micro-structured polydimethylsiloxane (PDMS) film embedded with a layer of multi-walled carbon nanotubes as the micro-structured conductive electrode, and a smooth PDMS film as the dielectric layer. The results indicate that the sensor exhibits a strong linear pressure-capacitance relationship. The sensitivity of the sensor can reach 1.3 kPa ⁻¹ in the pressure range of 0–100 Pa by optimizing the size of the electrode microstructure. In addition, the sensor exhibits a good repeatability even after 4000 repeated pressing. In addition, we demonstrate that the pressure sensor can be applied to monitor arterial pulse waves and breathing. The sensor is assembled in the form of arrays, which can effectively detect the shape of the measured object, proving that the sensor can be applied in complicated scenarios such as service robot and wearable equipment.

On the basis of the principal components analysis-particle swarm optimization-least squares support vector machine (PCA-PSO-LSSVM) algorithm, a fault diagnosis system is proposed for the compressor system. The relationship between the working principle of a compressor system, the fault phenomenon, and the root cause is analyzed. A fault diagnosis model is established based on the LSSVM optimized using PSO, the compressor fault diagnosis test experimental platform is used to obtain the fault signal of various fault occurrence states, and the PCA algorithm is employed to extract the characteristic data in the fault signal as input to the fault diagnosis model. The back-propagation neural network, the LSSVM algorithm, and the PSO-LSSVM algorithm are analyzed and compared with the proposed fault diagnosis model. Results show that the PCA-PSO-LSSVM fault diagnosis model has a maximum fault recognition efficiency that is 10.4% higher than the other three models, the test sample classification time is reduced by 0.025 s, the PCA algorithm can effectively reduce the input dimension, and the PSO-LSSVM fault diagnosis model based on the PCA algorithm for extracting features has a high recognition rate and accuracy. Therefore, the proposed fault diagnosis system can effectively identify the compressor fault and improve the efficiency of the compressor.