Cracked Blade Detection Method Through Data- and Decision-Level Vibration Fusion Framework

As the core rotating components of the air supply system, the compressor and its blades are prone to crack initiation under long-term cyclic loading and high-temperature erosion. Failure to detect cracks in a timely manner may lead to blade fracture, subsequently causing catastrophic unit failure. Affected by environmental noise and the weak vibration signatures of blade cracks, the existing methods for detecting blade cracks lack sufficient accuracy and reliability. To address this problem, a cracked blade detection method is proposed based on a vibration deep fusion framework for compressors. Specifically, the proposed vibration deep fusion framework includes two parts, namely data-level and decision-level fusion. The proposed data-level fusion method divides the original vibration signals into low-frequency and high-frequency bands based on the sampling frequencies of the two vibration sensors and fuses these two frequency bands to suppress noise and enhance defect features. Moreover, the proposed decision-level fusion method can further fuse the preliminary results obtained from the one-dimensional convolutional neural network with two raw vibration signals and the data-level fusion signal, thereby obtaining the final blade crack detection result. To verify the proposed method, a compressor blade crack detection experimental platform is built to simulate the crack detection effect of blades under different working conditions, which covers blades with artificial cracks of different lengths in noisy and noiseless environments. The results show that the proposed method has a detection accuracy of over 96% with 10-fold cross-validation, and the ablation experiment verifies that the proposed method is superior to traditional single signal or single-stage fusion methods, which demonstrates its effectiveness for compressor blade crack detection.