Rolling bearings are the core transmission components of large-scale equipment. Once a fault occurs, the consequences may be catastrophic, posing a serious threat to life and the safety of the property. Aimed at the problem of rolling bearing faults, this paper analyzes the characteristics of different fault signals and proposes a fault diagnosis method based on fuzzy signals. Based on the definition of an incomplete mapping of a new connotation, this paper proposes a fuzzy fault diagnosis method by fuzzy mapping, gives a fuzzy signal processing algorithm, and discusses two judgment principles. Further, it carries out a vibration signal analysis of the rolling bearing. According to the fault diagnosis method in this paper, the fault rolling bearing is diagnosed. The experimental results show that the proposed method can effectively diagnose rolling bearing faults closer to their natural attributes and solves the problem of traditional generator bearing fault diagnosis that requires complex models and poor diagnosis speed. Further, it can be seen that the average time consumption of this method is reduced and the fault recognition accuracy rate is increased. Compared with the existing related methods, this proposed diagnosis method is superior to that of several existing methods. It not only has higher precision, stronger anti-noise capacity, and faster diagnosis speed, but also has lower effective information loss.

In order to solve the problem that steel surface defects are easily covered or submerged by other objects or noise, this paper proposes an open–closed transformation algorithm which can eliminate or weaken multiple noises. In the case of a small number of samples, this paper establishes a super-resolution generative adversarial neural network to achieve the enhancement of sample data. For avoiding unrealistic image defects caused by cuts or brightness variations, an enhancement method is given which incorporates the original defective high-frequency information into classical image fusion methods, such as rotation and error slicing. Experimental results show that the accuracy of the proposed denoising method reaches over 90%, which is more than 2.6% of that of the most primitive classification network. To compare with existing denoising methods, the denoising method proposed in this paper not only has higher accuracy, faster denoising speed, and stronger anti-interference ability, but also has better adaptation to the environment. This research will provide a new solution method for the denoising of multi-noise phenomena in multiple different environments.

In order to solve the problem of automatically completing safety detection for construction sites and give an alert based on high-speed image streams, this paper proposes a violation of rules and regulations (VoRR) recognition method on a construction site and gives a matching method by automatically obtaining a few samples. The proposed safety detection method consists of five parts, which are redundant information reduction, classification, feature extraction, matching, inference rule and alarm alert. Compared with existing safety detection methods, the accuracy of the proposed method is increased by more than 9%. It not only has better performance, but also has more functions: reminding and alarming. For the subsequent establishment of an unmanned supervision system model on a construction site, this research will provide a new method of decision support, target detection, and recognition in multiple different scenarios.