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By using FLOD IoT sensors, you can locate any abnormal shocks occurring on your pipelines. The solution also allows you to locate the starting point of these shock waves and thus see the origin of the problem.
Pipeline structures are important structural elements that cannot be easily replaced. Damage to a structure will create significant safety risks and economic losses in a project.
Pipelines operate under difficult conditions, such as underground or in water. Cracks and leaks are likely to occur in response to external damage, which in turn causes major problems, such as property damage, environmental damage and loss of life. Regular monitoring and testing of pipelines in densely populated and environmentally protected areas is an effective means of ensuring the safe protection of pipelines.
A pipe condition monitoring system that detects and locates threats in the pipe before a real structural failure occurs, such as leaks, must target transient events such as impacts on the pipe wall or pressure waves propagating in the environment.
It is therefore extremely important to monitor the structures in advance in order to be able to respond effectively.
In recent years, the integrity of pipelines and piping systems in industrial plants has become a public concern as the global piping infrastructure grows and ages. In addition, to meet the increasingly high standards for technical safety, environmental friendliness and safe performance, the demand for a global pipeline monitoring system is expected to increase further in the future.
Ideally, such a system should be able to detect, classify and locate a wide range of threatening pipeline conditions before leaks appear along the entire length of the pipeline and in real time. In the long term, it should replace the time-consuming and costly routine inspections that currently take place at predetermined times.
The solution proposed by FLOD uses robust, autonomous and lightweight IoT sensors and artificial intelligence based on vibration analysis to detect and locate abnormal shocks or vibrations that may occur on pipelines.
If there is a difference between the signals depending on the position, our algorithm will be able to classify them and associate each with the correct position.