High acceleration and extreme load are frequently appeared on high-speed locomotion of legged robot’s legs, imposing a challenging trade-off between weight and torque in leg design. This paper proposes a new design paradigm based on cable-drive and elastic linkage to solve the problem. The details of the design procedure are given, including the construction of the single leg. With the optimum design of the linkage mechanism, a combined index of the workspace and tracking error are used as object function, and taking geometrical design parameters of the linkage as optimization parameters. Based on the target workspace and the spring-loaded inverted pendulum model, the best foot trajectory in obstacle climbing and trotting gait are analyzed and illustrated. This paper built linkage cable-drive spring robot based on the legged module integration. Simulations and experiments indicate that linkage cable-drive spring robot performs stable trotting with control of the spring-loaded inverted pendulum model. Linkage cable-drive spring robot prototype experiments results are provided to verify the validity of the new method.

The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets. Large-scale experimental methods with higher economic costs are often adopted to construct accurate performance models. To ensure the accuracy of the model and reduce the cost of the test, a novel method for modeling the performances of marine diesel engine is proposed based on deep neural network method coupled with virtual sample generation technology. Firstly, according to the practical experience, the four parameters including speed, power, lubricating oil temperature and pressure are selected as the input factors for establishing the performance models. Besides, brake specific fuel consumption, vibration and noise are adopted to assess the status of marine diesel engine. Secondly, small sample experiments for diesel engine are performed under multiple working conditions. Moreover, the experimental sample data are diffused for obtaining valid extended data based on virtual sample generation technology. Then, the performance models are established using the deep neural network method, in which the diffusion data set is adopted to reduce the cost of testing. Finally, the accuracy of the developed model is verified through experiment, and the parametric effects on performances are discussed. The results indicate that the overall prediction accuracy is more than 93%. Moreover, power is the key factor affecting brake specific fuel consumption with a weighting of 30% of the four input factors. While speed is the key factor affecting vibration and noise with a weighting of 30% and 30.5%, respectively.

In this paper, taking a certain type of high-power diesel engine exhaust valve abnormal wear phenomenon as an example, we conduct research on the exhaust valve surface micromorphology characteristics, contact surface accumulation products, additive transition layer, and combustion test. These passed diesel sulfur content comparative test, diesel additive composition analysis, and high-sulfur-content and low-sulfur-content diesel and diesel oil additive action test. At present, there is no authoritative research on the influence of sulfur content in diesel on valve seat wear of high-power diesel engines, and the protection mechanism of diesel additives on the valve seat is not clear. The sulfur in diesel, like the lead in gasoline, has long been known to resist wear in the valve seats of high-power diesel engines; just as gasoline additives compensate for the loss of lead, diesel oil additives seem to compensate for the loss of sulfur. Tests show that a uniform carbon deposition layer is formed on the contact surface after the diesel is burned, and the carbon deposition layer is more densely and uniformly adsorbed on the contact surface under the action of diesel additives to form an antiwear layer. Tests also show that the sulfur in diesel has no effect on the wear resistance of the valve seat.

In recent years, considerable attention has been paid in time–frequency analysis (TFA) methods, which is an effective technology in processing the vibration signal of rotating machinery. However, TFA techniques are not sufficient to handle signals having a strong non-stationary characteristic. To overcome this drawback, taking short-time Fourier transform as a link, a TFA methods that using the generalized Warblet transform (GWT) in combination with the second order synchroextracting transform (SSET) is proposed in this study. Firstly, based on the GWT and SSET theories, this paper proposes a method combining the two TFA methods to improve the TFA concentration, named GWT–SSET. Secondly, the method is verified numerically with single-component and multi-component signals, respectively. Quantized indicators, Rényi entropy and mean relative error (MRE) are used to analyze the concentration of TFA and accuracy of instantly frequency (IF) estimation, respectively. Finally, the proposed method is applied to analyze nonstationary signals in variable speed. The numerical and experimental results illustrate the effectiveness of the GWT–SSET method.