The degradation of a machine is nonlinear, which brings challenges to its performance assessment during condition monitoring, especially when there is a run-in period. Technically, the quantification of mechanical degradation is to define a distance metric from a health baseline. This paper develops an integrated condition monitoring scheme, where the degradation evaluation and fault diagnosis are combined by using one technical framework. Specifically, an optimum healthy state (OHS) is determined based on the clustering center of the self-organizing map (SOM) neural network instead of the commonly used initial working state. Then, the distance metric deviating from the OHS is defined as a health index, where the perceptual vibration hashing is improved to make it more sensitive to degradation. Visualized fault diagnosis is carried out by the SOM when the health index exceeds the preset threshold. Two cases with experiments are conducted to demonstrate the accuracy and robustness of the proposed method.

To address the strong coupling, nonlinearity, and disturbances caused by microbial biochemical reactions in temperature and humidity control during soy sauce koji production, this study presents an engineering application and comparative investigation of three progressive control strategies: feedforward decoupling PI (FFD-PI) control, particle swarm optimization-based PI (PSO-PI) control, and fuzzy gain-scheduled PI (FGS-PI) control. A dynamic temperature–humidity model is first identified, and a multi-input multi-output closed-loop system based on PI control is established. Feedforward decoupling is then introduced to reduce the interactions between the temperature and humidity control loops, followed by offline optimization of the two PI controllers using particle swarm optimization. Based on the optimized parameters, fuzzy gain scheduling is further applied to adjust the controller gains online. Simulation results show that all three strategies achieve effective temperature and humidity setpoint tracking. PSO-PI improves the dynamic response and disturbance rejection capability, whereas fuzzy gain-scheduled PI provides better overshoot suppression and lower error indices. Under the koji-turning disturbance, the temperature and humidity overshoots are reduced by 62.2% and 48.3%, respectively. These results indicate that the PSO-initialized fuzzy gain-scheduled PI method improves temperature and humidity control performance and provides a useful reference for the intelligent control of koji production environments.

Stainless steel bipolar plates play a key role in solid oxide fuel cells as a supporting part of solid oxide fuel cells. In order to obtain the variation of the resistance of nickel–cobalt-coated stainless steel bipolar plates with temperature, a measurement test platform for the resistance of high-temperature bipolar plates was built, and the resistance of the bipolar plates with temperature was measured by the four-terminal resistance measurement method, and the resistance values of the solid oxide fuel cell bipolar plates at different temperatures were measured. The results show that the resistance of the stainless steel bipolar plate specimen of solid oxide fuel cell gradually decreases during the temperature increase. When the temperature of the solid oxide fuel cell stainless steel bipolar plate specimen is increased to 800 °C, the resistance value is 0.6 Ω and the resistivity is 0.24 Ω m.

The bending behavior of the hydraulic pipeline on the roller of the logging winch will cause a certain plastic deformation of the hydraulic pipeline, which has a certain influence on its performance. In view of the above problems, this paper uses finite element analysis software to analyze the plastic deformation law of hydraulic pipelines under different bending radii. It can be found from the analysis results that during the bending process, the plastic deformation of the metal continuous capillary gradually increases with the neutral layer outward. In the range of the bending radius from 75 mm to 150 mm, with the increase of the bending radius, the equivalent plastic deformation decreases from 3.5×10^-2 to 1.46×10^-2. The changing trend of life also tends to be gentle with the increase of the bending radius, and the number of cycles increases from 25000 to 398107. The analysis of the plastic deformation law and the life of hydraulic pipelines provides a reference for designing winch drums.

Vent holes in tire molds typically exhibit large depth-to-diameter ratios (25–50) and variable drilling angles, both of which increase the risk of drill-bit breakage during automated drilling. To address this problem, this study develops a robotic drilling system consisting of a 6-DOF industrial robot and a dedicated end effector integrating a spindle unit, a linear feed unit, and a telescopic drill-bushing unit. A GRU-based feed-torque model predictive control method (GFTMPC) is proposed for robotic small-diameter deep-hole drilling, which achieves flexible control by integrating angle-aware feed-torque modeling with constrained MPC-based feed-rate optimization. The resulting GRU-based feed-torque model (GFTM) is embedded in the MPC framework for torque prediction and achieves an R² value of 0.9682. Under identical simulation conditions, GFTMPC reduces the RMSE of the feed-rate increment by 34.82% and the saturation ratio of the feed-rate increment by 90.78% relative to a PID baseline, indicating smoother feed regulation and fewer abrupt control actions in simulation. Comparative engineering experiments further suggest that, under the tested robotic configurations, adaptive feed-rate regulation by GFTMPC is an important contributor to improved tool life and drilling reliability. Hole-diameter measurements show deviations ranging from +0.03 mm to +0.11 mm, which were considered acceptable for the subsequent work steps in this application. Engineering application results show that robotic drilling increases daily throughput per worker by 71.38% and the average number of holes drilled per bit by 237%.