Research on rapier loom fault system based on cloud-side collaboration

The electrical control system of rapier weaving machines is susceptible to various disturbances during operation and is prone to failures. This will seriously affect the production and a fault diagnosis system is needed to reduce this effect. However, the existing popular fault diagnosis systems and methods need to be improved due to the limitations of rapier weaving machine process and electrical characteristics. Based on this, this paper presents an in-depth study of rapier loom fault diagnosis system and proposes a rapier loom fault diagnosis method combining edge expert system and cloud-based rough set and Bayesian network. By analyzing the process and fault characteristics of rapier loom, the electrical faults of rapier loom are classified into common faults and other faults according to the frequency of occurrence. An expert system is built in the field for edge computing based on knowledge fault diagnosis experience to diagnose common loom faults and reduce the computing pressure in the cloud. Collect loom fault data in the cloud, train loom fault diagnosis algorithms to diagnose other faults, and handle other faults diagnosed by the expert system. The effectiveness of loom fault diagnosis is verified by on-site operation and remote monitoring of the loom human-machine interaction system. Technical examples are provided for the research of loom fault diagnosis system.

Design Specification for Network Fault Diagnosis System

As the network becomes more and more complex and heterogeneous, the problem of network management becomes more and more prominent. The research and application of network management is of great significance for ensuring the normal operation of network and improving the reliability and availability of network system. The main problems of network management are network configuration, failure, performance, security, planning and scaling, etc. On the basis of in-depth research and discussion on network management and fault management, this paper designs the system requirements of network fault diagnosis system.

Application of Computer Algorithm in Fault Diagnosis System of RM Equipment

At present, modern mechanical equipment is gradually developing towards large-scale and intelligent, which leads to more and more complex equipment structure. Therefore, people have higher and higher requirements for intelligent fault diagnosis of mechanical equipment, which leads to the application of various algorithms to mechanical equipment. Among them, rotating machinery (hereinafter referred to as RM) mainly relies on rotating action to complete specific functions, such as gearbox, gas turbine, generator and engine, which are often the core components of mechanical equipment. Therefore, the FSGS (hereinafter referred to as FSGS) of RM equipment has become a very key link in system design and maintenance, which requires designers to constantly overcome the original intelligent diagnosis system. Through a variety of deep learning algorithms, we can improve the diagnosis efficiency of automatic monitoring and diagnosis equipment, which can also reduce the loss caused by untimely diagnosis. Firstly, this paper analyzes the types of application of computer algorithms in the fault body segment system of RM equipment. Then, this paper analyzes an algorithm, which can better improve the diagnosis efficiency of the equipment.

Research on Pump Product Intelligent Fault Diagnosis System Based on Dig Data

With the integration of information technology and manufacturing industry and the improvement of equipment monitoring data and computer computing ability, equipment fault diagnosis has entered the era of “big data”. Using big data analysis method for fault diagnosis, the fault diagnosis model can learn its own characteristics and complete fault identification, so that the fault diagnosis is more intelligent and automatic on the basis of high identification accuracy. This paper mainly studies the application of fault diagnosis method and big data analysis method in pump product fault diagnosis.

Technology development and commercial applications of industrial fault diagnosis system: a review

Machinery will fail due to complex and tough working conditions. It is necessary to apply reliable monitoring technology to ensure their safe operation. Condition-based maintenance (CBM) has attracted significant interest from the research community in recent years. This paper provides a review on CBM of industrial machineries. Firstly, the development of fault diagnosis systems is introduced systematically. Then, the main types of data in the field of the fault diagnosis are summarized. After that, the commonly used techniques for the signal processing, fault diagnosis, and remaining useful life (RUL) prediction are discussed, and the advantages and disadvantages of these existing techniques are explored for some specific applications. Typical fault diagnosis products developed by corporations and universities are surveyed. Lastly, discussions on current developing situation and possible future trends are in the CBM performed.