Luminescence and electrical insulation properties of rare earth doped strontium aluminate elastomer intelligent sensing material

The traditional stress detection methods for electrical equipment are mainly achieved through mechanical or electrical sensing technologies, which face drawbacks such as electromagnetic interference (EMI), muting sensitivity, and potential insulation damage of the equipment itself. In this work, a flexible thin composite material with excellent mechanical luminescence properties and insulation performance is proposed, which can be used for mechanical stress monitoring of power equipment and can also indirectly achieve damage warning. Its advantage lies in visually reflecting stress variations through a visualization mechanism, while the sensing material itself maintains excellent insulation properties, avoiding damage to the original insulation caused by the embedded sensors. Elastomeric composite materials capable of exhibiting mechanoluminescence are prepared by blending SrAl2O4:Eu2+Dy3+(SAOED) particles with ethylene propylene diene monomer (EPDM) rubber at different mass ratios. The effects of SAOED particle content on the luminescence, mechanical and electrical properties of the composites are investigated. The mechanoluminescence performance demonstrate that the elastomeric composite with 40% SAOED particle exhibits superior mechanoluminescence properties and maintains excellent electrical insulation performance. The stress required for the mechanoluminescence is 0.96 MPa, which is easier to produce luminescence than the composite material with 25% SAOED content. The mechanoluminescence intensity of the composite material with 40% SAOED content is 37.4% higher than that of the composite material with 25% SAOED content. The electrical performance shows that the volume resistivity is 8.711×1011Ω·m at power frequency. The breakdown strength is 92.51 kV/mm that it can meet the insulation requirements of power equipment. Finally, the luminescence mechanism of the composite is further analyzed using the carrier detrapping band model. This work provides a novel idea for the development of intelligent sensing for stress detection of power equipment and has broad potential application prospects.