Multi-objective acoustic sensor placement optimization for crack detection of compressor blade based on reinforcement learning

Nowadays, acoustic sensors have been widely applied for structural health monitoring and crack detection of compressor blades. As the detection accuracy is mainly affected by signal quality, the optimal sensor placement (OSP) is significant for crack detection. To search the OSP for reliable signals, the multi-objective acoustic sensor placement optimization method is proposed based on information field fitting and optimization with reinforcement learning. First, the improved ridge and least squares support vector regression (R-LS-SVR) method is presented to fit information field of acoustic signal. Besides, the multi-objective function is constructed based on the compressor operation conditions, sound quality and crack damage observation. In addition, the Pareto front is obtained to realize multi-objective optimization (MOO) based on non-dominated sorting genetic algorithms (NSGA-II). Furtherly, the reinforcement learning-based pattern search method (RL-PSM) is proposed to quickly search the OSP of acoustic sensor. The compressor experiments are implemented to test the proposed method, and it can detect crack with the accuracy of 99.17%, which is superior to other locations. Comparing with other fitting and optimization methods, the advantage of the proposed method is validated for OSP optimization and crack detection.