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Sensory data are the basis for the intelligent health state awareness of planetary gearboxes, which are the critical components of electromechanical systems. Despite the advantages of intelligent diagnostic techniques for detecting intricate fault patterns and improving diagnostic speed, challenges still persist, which include the limited availability of fault data, the lack of labeling information and the discrepancies in features across different signals. Targeting this issue, a subdomain distribution adversarial adaptation diagnosis method (SDAA) is proposed for faults diagnosis of planetary gearboxes across different conditions. Firstly, nonstationary vibration signals are converted into a two-dimensional time–frequency representation to extract intrinsic information and avoid frequency overlapping. Secondly, an adversarial training mechanism is designed to evaluate subclass feature distribution differences between the source and target domain. A conditional distribution adaptation is employed to account for correlations among data from different subclasses. Finally, the proposed method is validated through experiments on planetary gearboxes, and the results demonstrate that SDAA can effectively diagnose faults under crossing conditions with an accuracy of 96.7% in diagnosing gear faults and 95.2% in diagnosing planet bearing faults. It outperforms other methods in both accuracy and model robustness. This confirms that this approach can refine domain-invariant information for transfer learning with less information loss from the sub-class level of fault data instead of the overall class level. [ABSTRACT FROM AUTHOR]

Improving prefabricated buildings represents an effective means of promoting the industrialization of construction engineering, with construction quality playing a pivotal role in the successful implementation of projects. A total of 16 main factors affecting the quality of prefabricated buildings in the implementation process were identified through expert questionnaires and case studies. The magnitude of the influence of each factor on the quality of prefabricated buildings was then analyzed by setting up weights and hierarchical levels. This provided a basis for the development of quality assurance measures. The results indicate that the installation of locating reinforcement bars in the conversion layer, the installation of prefabricated walls, the installation of laminated panels and pre-embedded bolts, and the sealing and grouting operation have a greater impact on the overall quality of the project. Additionally, the on-site construction organization is identified as the most important factor. [ABSTRACT FROM AUTHOR]

Rotating machinery fault diagnosis is of key significance for ensuring safe and efficient operation of various industrial equipment. However, under nonstationary operating conditions, the fault–induced characteristic frequencies are often time–varying. Conventional Fourier spectrum analysis is not suitable for revealing time–varying details, and nonstationary fault feature extraction methods are still in desperate need. Order spectrum can reveal the rotational–speed–related time–varying frequency components as spectral peaks in order domain, thus facilitating fault feature extraction under time–varying speed conditions. However, the speed–unrelated frequency components are still nonstationary after angular–domain resampling, thus causing wide–band features and interferences in the order spectrum. To overcome such a drawback, this work proposes a rotating machinery fault diagnosis method based on adaptive separation of time–varying components and order feature extraction. Firstly, the rotational speed is estimated by the multi–order probabilistic approach (MOPA), thus eliminating the inconvenience of installing measurement equipment. Secondly, adaptive separation of the time–varying frequency component is achieved through time–varying filtering and surrogate test. It effectively eliminates interference from irrelevant components and noise. Finally, a high–resolution order spectrum is constructed based on the average amplitude envelope of each mono–component. It does not involve Fourier transform or angular–domain resampling, thus avoiding spectral leakage and resampling errors. By identifying the fault–related spectral peaks in the constructed order spectrum, accurate fault diagnosis can be achieved. The Rényi entropy values of the proposed order spectrum are significantly lower than those of the traditional order spectrum. This result verifies the effective energy concentration and high resolution of the proposed order spectrum. The results of both numerical simulation and lab experiments confirm the effectiveness of the proposed method in accurately presenting the time–varying frequency components for rotating machinery diagnosing faults. [ABSTRACT FROM AUTHOR]

The friction plate gap as a key parameter which affects the towing torque is got by analyzing friction state of the hydro- viscous clutch and building mathematical model of the towing torque which combines with the specific parameter of the hydro- viscous clutch. The accuracy of the towing torque calculation model is verified by analyzing friction characteristic of hydro- viscous clutch and experimental study. The test of impact output torque is carried out which including towing torque,temperature and difference speed between two friction materials. It can be found through the test the towing torque of copper- based friction plate is about 2 times of the carbon- based friction plate with the same structure and the temperature of copper- based friction plate is higher than the carbon- based friction plate under the same experimental conditions and the change of friction coefficient with the difference speed is large. The relationship between the friction coefficient and the difference speed is fitted.

To improve the safety management level of construction projects, the housing construction project was taken as the research object and the hierarchical structure model by using the improved analytic hierarchy process (IAHP) was established. Based on the four dimensions of human, material, environment, management, the 12 influencing factor evaluation indexes and 34 scoring items were designed. By the redistribution of weight, the value range of scoring items was determined, and then the construction safety management evaluation system was constructed to evaluate the project safety management. When evaluating multiple projects, if the overall evaluation conclusion is poor, the technique for order preference by similarity to an ideal solution (TOPSIS) was used to evaluate the relative advantages and disadvantages of the project by introducing reference interval α[0.5,0.6]. By calculating the membership relationship between nearness degree Li+ and reference interval α, the decision‐making method to improve the level of project safety management was given. The results show that, if Li+>0.6,need external interactive learning, if Li+<0.5, internal communication and learning can be organized. If Li+∈α,the project safety management level can be improved by the combination of external interactive learning and internal experience exchange. [ABSTRACT FROM AUTHOR]