Port machinery forgings are the key parts in the process of ship manufacturing, and their quality and performance directly affect the safety and stability of ship operation. Due to the complex and changeable working environment of mechanical forgings in ship ports, such as under alternating loads, high temperature and high pressure and other harsh conditions, various failures are prone to occur. In view of these faults, this paper aims to discuss the fault diagnosis and prediction of mechanical forgings in ship ports, in order to improve the accuracy of fault diagnosis and the timeliness of prediction.
With the development of technology, the research on fault diagnosis and prediction of ship port machinery forging has made a lot of progress. In terms of fault diagnosis, researchers have proposed a variety of methods, including analysis based on vibration signals, diagnosis based on temperature monitoring, and pattern recognition based on neural networks and deep learning. These methods improve the accuracy and efficiency of fault diagnosis in different degrees. However, there are still some limitations in the existing research, such as insufficient consideration of complex operating conditions and multiple fault types, as well as the quality of monitoring data and the development of processing technology.
In this paper, the fault diagnosis and prediction of ship port mechanical forgings are discussed by the method of literature review and experimental research. First of all, the relevant literature is collected and sorted out, and the methods, advantages and disadvantages of the existing research are analyzed. Secondly, aiming at the shortcomings of existing researches, a fault diagnosis and prediction scheme based on multi-source information fusion is designed. In this scheme, steps such as data acquisition, data preprocessing, fault feature extraction and pattern recognition are adopted to achieve accurate diagnosis and prediction of mechanical forging faults in ship ports.
The experimental results show that the method proposed in this paper is effective in the fault diagnosis and prediction of ship port machinery forgings. The experimental results show that the accuracy of the proposed method in fault classification and prediction is more than 90%, which is about 10% higher than the traditional method. In addition, the sensitivity and specificity of the proposed method are both high, indicating that it can identify different fault types and degrees well.
In the course of the experiment, we also further analyzed the main reasons leading to the failure. The results show that material fatigue, overload damage, poor lubrication and other factors are the main causes of mechanical forging failure. In view of these problems, we put forward the corresponding prevention and improvement measures to reduce the risk of failure.
In this paper, a fault diagnosis and prediction scheme based on multi-source information fusion is proposed by studying the fault diagnosis and prediction of ship port machinery forging. The experimental results show that the scheme has high accuracy in fault classification and prediction, and has a good application prospect.
In view of the shortcomings of existing research, future research can be carried out from the following aspects: First, in-depth study of multi-fault diagnosis and prediction methods to adapt to more complex and changeable working conditions; Secondly, the development of monitoring data processing technology is strengthened to improve the accuracy of fault feature extraction and pattern recognition. Finally, the application of new materials and processes in the manufacture of mechanical forgings in ship ports is explored to improve their durability and reliability.
In short, fault diagnosis and prediction of mechanical forgings in ship ports is a hot and difficult point in current research, which is worthy of further discussion. By improving the existing diagnosis and prediction methods and improving the timeliness and accuracy of fault handling, the safety and stability of ship operation can be effectively guaranteed.
