The normal operation of the engine is of great importance for the safety of life and property, so we need to monitor and analyze the state of the engine. Most of the existing methods only diagnose the type of engine fault without further analysis of the severity of the engine fault. Additionally, the features used for fault diagnosis are not selected according to faults and do not necessarily contain more fault information. In the paper, we propose using Pearson correlation coefficients in combination with faults selects sensors and the corresponding features, and then single-fault diagnosis combined with GRU (gating recurrent unit) is performed by using the selected sensors and features. Since multi-fault diagnosis is more difficult than single-fault diagnosis, more state information is required. Therefore, the multi-fault diagnosis will directly extract the time domain features screened above from all vibration signals, stack them and send them to GRU for multi-fault diagnosis. From the experimental results we can conclude that the feature selection method combining Pearson correlation coefficient and fault state can extract effective features to diagnose the fault type and its severity. Finally, the influence factors of the model are analyzed through comparative experiments, and the results show the effectiveness of the method and the selected model parameters.

This paper presents a rigid-flexible composite of bionic hand structure design scheme solution for solving the problem of low load on the soft gripping hand. The bionic hand was designed based on the Fast Pneumatic Network (FPN) approach, which can produce a soft finger bending drive mechanism. A soft finger bending driver was developed and assembled into a human-like soft gripping hand which includes a thumb for omnidirectional movement and four modular soft fingers. An experimental comparison of silicone rubber materials with different properties was conducted to determine suitable materials. The combination of 3D printing technology and mold pouring technology was adopted to complete the prototype preparation of the bionic hand. Based on the second-order Yeoh model, a soft bionic finger mathematical model was established, and ABAQUS simulation analysis software was used for correction to verify the feasibility of the soft finger bending. We adopted a pneumatic control scheme based on a motor micro-pump and developed a human–computer interface through LabView. A comparative experiment was carried out on the bending performance of the finger, and the experimental data were analyzed to verify the accuracy of the mathematical model and simulation. In this study, the control system was designed, and the human-like finger gesture and grasping experiments were carried out.

Incorporating linear-scanning micro-electro-mechanical systems (MEMS) micromirrors into Fourier transform spectral acquisition systems can greatly reduce the size of the spectrometer equipment, making portable Fourier transform spectrometers (FTS) possible. How to minimize the tilting of the MEMS mirror plate during its large linear scan is a major problem in this application. In this work, an FTS system has been constructed based on a biaxial MEMS micromirror with a large-piston displacement of 180 μm, and a biaxial H∞ robust controller is designed. Compared with open-loop control and proportional-integral-derivative (PID) closed-loop control, H∞ robust control has good stability and robustness. The experimental results show that the stable scanning displacement reaches 110.9 μm under the H∞ robust control, and the tilting angle of the MEMS mirror plate in that full scanning range falls within ±0.0014°. Without control, the FTS system cannot generate meaningful spectra. In contrast, the FTS yields a clean spectrum with a full width at half maximum (FWHM) spectral linewidth of 96 cm⁻¹ under the H∞ robust control. Moreover, the FTS system can maintain good stability and robustness under various driving conditions.

This paper is based on the “Fast Pneumatic Mesh Driver” (FPN) used to couple a silicone rubber soft body with a rigid skeleton. A rigid-flexible coupling soft-body human-like finger design scheme is proposed to solve the problem of low load on the soft-body gripping hand. The second-order Yeoh model is used to establish the statics model of the soft humanoid finger, and the ABAQUS simulation analysis software is used for correction and comparison to verify the feasibility of the soft humanoid finger bending. The thickness of the driver cavity and the confining strain layer were determined by finite element simulation. The mold casting process is used to complete the preparation of human-like fingers and design a pneumatic control system for experiments combined with 3D printing technology. The experimental results show that the proposed rigid-flexible coupling soft body imitating the human finger structure can realize the corresponding actions, such as the multi-joint bending and side swinging, of human fingers. Compared with the traditional pure soft-body finger, the fingertip output force is significantly improved. The optimal design and simulation analysis of the human gripper and the feasibility of the application have practical guiding significance.

Intelligent manufacturing has a strong role in promoting the transformation and upgrading of traditional industries such as the chair industry. This study aimed to accurately evaluate the production status and technical level of chair industry enterprises, and then better guide chair industry enterprises to gradually implement intelligent manufacturing. Based on the analytic network process (ANP), we propose four capability domains, nine capability sub-domains, and 21 evaluation elements, thereby constructing an evaluation model for the capability maturity of chair industry enterprises’ intelligent manufacturing. First, the weight relationship of each index of the model was determined by means of an expert questionnaire. Then, super decision software was used to complete the modeling of the evaluation index of the network analytic hierarchy process. Finally, the evaluation model of the intelligent manufacturing maturity of chair industry enterprises was applied to 50 chair industry enterprises for evaluation and verification, and the evaluation results of the model proposed in this paper were compared with the evaluation results of the intelligent manufacturing maturity model released by China’s national standards. The results show that the evaluation model constructed in this study can better reflect the development status and overall technical level of intelligent manufacturing in the chair industry. Furthermore, the evaluation results can provide decision-making suggestions for chair industry enterprises to identify important areas for improvement and implementation of intelligent manufacturing upgrade plans.