It can detect belt tears in time to avoid material leakage from the tear and reduce material waste and loss. For example, in the chemical industry, the transported materials often have high value. Through the belt tear detection system, the cost of material loss can be effectively reduced.

When the belt tear is not detected in time, it may lead to further damage to the belt and even cause other equipment failures. The belt tear detection system can provide early warning, allowing the staff to take timely measures to reduce the risk of equipment damage and prolong the service life of the equipment. For example, in the raw material conveying belt of the cement plant, the belt tear is detected in time to avoid the damage of the crusher and other equipment caused by the belt failure.

Prevent material leakage from posing safety threats to workers and ensure the safety environment of the production site. For example, in the conveying belt system in the mine, the application of the belt tear detection system effectively avoids safety accidents caused by material leakage.

In some industrial sites, there are complex environments such as high temperature, high humidity, dust, mud, and electromagnetic interference, which may affect the normal operation of sensors and monitoring equipment, resulting in inaccurate test results. For example, in the high-temperature material conveying belt scene of a steel plant, high temperature may degrade the performance of the sensor and affect the accuracy of belt tear detection.

The belt materials and operating conditions used in different industries vary, such as rubber belts, nylon belts, etc., as well as different conveying speeds and loads, which increase the versatility and adaptability of the detection algorithm. For example, in the food industry, the food-grade belts used are very different from industrial belts in terms of material and operating requirements, and the detection algorithm needs to be optimized specifically.

For complex environmental disturbances, sensors and monitoring equipment with resistance to high temperature, high humidity, dust, and electromagnetic interference are selected, and shielding and filtering measures are taken to reduce the impact of environmental factors on the detection results. For example, in high-temperature environments, use high-temperature fiber optic sensors and insulate the sensors; in environments with strong electromagnetic interference, use shielded cables and anti-interference monitoring equipment.

Through a large number of experiments and data collection, the detection algorithm is trained and optimized for different belt materials and working conditions to improve the versatility and adaptability of the algorithm. For example, the deep learning algorithm is used to learn and train belt tear samples under different materials and working conditions, so that the algorithm can accurately identify belt tears in various situations. At the same time, combined with the adaptive algorithm, the detection parameters are automatically adjusted according to the real-time operating parameters of the belt to improve the accuracy of detection.