Fused machine learning based on Boosting algorithm for noise recognition of moving parts in sealed cavity.

• An experimental system for noise recognition based on PIND system is established. • The proposed method improves the recognition effect by fusing the feature extraction capabilities of multiple individual models. • Creatively adding the recognition results of each layer to the original samples as new features. The detection and identification of the noise of the movable parts in a sealed cavity are of great reference significance for the fault analysis of the sealed equipment. However, traditional machine learning algorithms have the problem that it is difficult to distinguish the noise signal generated by the internal movable parts from the collision signal generated by the movable objects in a sealed cavity. Therefore, this paper proposes a fusion machine learning (FML) method based on Boosting algorithm to improve the accuracy of noise signals recognition. First of all, data were collected using the experimental platform, and then the feature selection was performed to establish the noise signal datasets. Finally, the noise identification of the internal movable parts was realized by FML, and its parameters were optimized. FML consists of a three-layer algorithm. The first layer recognition algorithm is used to identify the noise data set D1, and then adds its noise recognition result as a new feature to the original sample to form the data set D2. The second layer algorithm is used to identify D2 and then add the output noise recognition result to the data set D2 as a new feature to form D3. The third layer algorithm is used to identify noise in data set D3. Simulation experiments are carried out in this paper to verify the proposed method's effectiveness. The results show that the recognition accuracy of FML is 7.6 % higher than that of the traditional Stacking algorithm. The training time of the Stacking algorithm is more than 2 times higher than that of FML and more than 4 times higher on UCI datasets. In addition, the algorithm is also significant in innovating fusion methods and expanding the scope of adaptation. [ABSTRACT FROM AUTHOR]