Real-time online resistance-alteration-based multiple-fault diagnosis framework and implementation for mine ventilation systems.

• A real-time online fault diagnosis framework for simultaneous diagnosis of normal resistance alterations and resistance-alteration-based faults is proposed. • Based on the observability of resistance, the layout method of the node wind pressure sensor is proposed. • It is proved that the mine ventilation network can be divided into linear topology and star topology. • A multiple faults diagnosis algorithm for ventilation system with continuous observation and dynamic update is proposed. • The effectiveness of the framework and algorithm is verified in the coal mine ventilation system. Resistance-alteration-based multiple-fault diagnosis of mine ventilation systems is essential to ensuring the safety of mine production. The basic assumptions, definitions, framework, theory, algorithms and experiments regarding the resistance-alteration-based multiple-fault diagnosis of mine ventilation systems are systematically studied here. First, the problems of the single-fault assumption in the conventional ventilation system fault diagnosis framework are analyzed, and a real-time online multiple-fault diagnosis framework is proposed. Then, based on the theory of resistance observability, the sensor layout scheme is optimized, the properties of the ventilation subnetwork are studied, and the interpretable resistance-alteration-based multiple-fault diagnosis (RMFD) algorithm is designed. Finally, a diagnosis experiment with multiple faults was carried out for a real coal mine. The experimental results show that the RMFD algorithm can achieve 100% accuracy of identification and positioning at the ventilation subnetwork level and can perform quantitative resistance-alteration analysis for k − 1 roadways within a k -order star subnetwork, which verifies the effectiveness of the real-time online multiple-fault diagnosis framework and the RMFD algorithm. This study achieves real-time online multiple-fault diagnosis of mine ventilation systems and provides a theoretical reference and technical support for the intelligentization of mine ventilation systems and similar fluid networks. [ABSTRACT FROM AUTHOR]