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The safety and stability of the rapier loom during operation directly impact the quality of the fabric. Therefore, it is of great significance to carry out fault diagnosis research on rapier looms. In order to solve the problems of low diagnosis efficiency, untimely diagnosis, and high maintenance cost of existing rapier looms in manual troubleshooting of loom failures. This paper proposes a new intelligent fault diagnosis method for rapier looms based on the fusion of expert system and fault tree. A new expert system knowledge base is formed by combining the dynamic fault tree model with the expert system knowledge base. It solves the problem that the traditional expert system cannot achieve precise positioning in the face of complex fault types. Construct the rapier loom’s fault diagnosis model, build the intelligent diagnosis platform, and finally realize the intelligent fault diagnosis of the rapier loom. Experimental results show that the algorithm can quickly diagnose and locate rapier loom faults. Compared with the current intelligent diagnosis algorithm, the algorithm structure is simplified, which provides a theoretical basis for the broad application of intelligent fault diagnosis on rapier looms.

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Algal inhibiting materials were synthesized by the co-impregnation method. Cerium was taken as an algal inhibiting agent with copper/sepiolite utilized as the carrier. The algal growth inhibition was assessed based on the form of presence and distribution of Cu 2+ and Ce 3+ ions on the surface of sepiolite using X-ray diffraction (XRD) and SEM analysis. The results indicate that at 5 wt. % Cu content, 0.8 wt. % Ce and after heat treatment at 400 °C, the materials exhibit the best algal inhibiting capacity of 83.11%.