Abstract
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.
Highlights
We study a collaborative cloud-edge rapier loom fault system that performs edge computing to troubleshoot common faults at the industrial site and other fault diagnostics in the cloud
When edge computing cannot effectively diagnose faults, optimized fault diagnosis algorithms are trained in the cloud to handle fault problems using the cloud-based fault diagnosis system
The expert system built in the field is able to handle common fault problems of rapier looms
Summary
Fault diagnosis techniques for equipment have prominent applications in electric power systems [1, 2], chemical process systems [3, 4], photovoltaic systems [5–9], bearings [10, 11], building energy systems [6], control systems [12, 13], and automation equipment [14]. The electrical control system of rapier loom is susceptible to various disturbances during operation and is prone to malfunction. This will seriously affect the production and a fault diagnosis system is required to reduce this effect. Due to the lack of sufficient computing and storage resources for weaving machine equipment, weaving machine fault diagnosis systems need to process fault data in the cloud using IoT technology. With the development of IoT technology, the number of connected devices has increased, operational data has increased significantly, and cloud-based applications are expanding [15].
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