Intelligent detection technology for leakage bag of baghouse based on distributed optical fiber sensor

•Distributed optical fiber sensing system monitors broken filter bags in bag filter.•The broken filter bag is recognized and positioned in real time online.•Entropy and energy ratio can describe signal characteristics in broken filter bag.•BP neural network classifier efficiently identifies the broken filter bags. In order to overcome the deficiency of traditional methods for damaged bag identification and location of baghouse in environmental protection industry, a real-time online identification and location system for leak bag of bag filter based on distributed optical fiber sensing technology of phase-sensitive optical time-domain reflectometer (Φ-OTDR) is proposed. Through the design of laying mode of optical fiber in filter bag of baghouse, the program flow of identifying and locating damaged filter bag is designed, and the real-time monitoring and positioning of filter bags is realized by distributed optical fiber sensing system. Wavelet packet decomposition (WPD) method is used to obtain the energy spectrum and energy entropy. Taking the energy ratio of the third sub-band frequency signal and the energy entropy as eigenvectors, the characteristic differences of optical fiber vibration signals between different types of good bags and different types of damaged bags with different leakage hole size and position are analyzed. The results show that the eigenvectors extracted by the method used in this paper can effectively describe the characteristics of the airflow vibration signals in the filter bags. Back propagation (BP) neural network algorithm is used to recognize the optical fiber vibration signals in damaged bags with different leakage hole sizes and positions. In addition, quantitative analysis method is used to identify the sample signals in different types of damaged bags. First, the airflow vibration signals in damaged bags with different leakage hole positions are identified while the leakage hole size remains the same. Second, the airflow vibration signals in leakage bags with different leakage hole sizes are identified while the leakage hole position remains the same. Finally, the BP neural network classifier is trained by the characteristics of the vibration signals in a filter bag, and it is used to identify the remaining field testing signals. Each type of vibration event is identified 10 times and the average recognition rate is calculated. The results show that the proposed classifier maintains high recognition stability and a high