An Efficient I-PixelHop Framework Based on Spark-GPU for Intelligent Fault Diagnosis

I-PixelHop is a lightweight learning framework based on successive subspace learning. Compared with the deep learning methods, the rolling bearing fault diagnosis (RBFD) model based on I-PixelHop has lower computational complexity and smaller model size, and it can obtain a good diagnosis accuracy in the complex working conditions. However, the training and diagnosis time of the RBFD model based on I-PixelHop are still long in the face of large-scale RB fault datasets. Therefore, an efficient I-PixelHop framework based on Spark-GPU for intelligent fault diagnosis is proposed. First, a Spark-GPU-based distributed parallel I-PixelHop framework is developed, which can efficiently perform distributed parallel training and diagnosis. Second, an asynchronous parallel execution strategy based on superscalar pipeline (APES-SP) is proposed, which can reduce the waiting time of each functional unit of the distributed parallel I-PixelHop framework. Finally, an ensemble classifier based on Bagging is designed and parallelized, which can improve the diagnosis accuracy of the RBFD model based on distributed parallel I-PixelHop framework. Extensive experiments demonstrate that the proposed framework can not only maintain high diagnosis accuracy but also significantly improve the training performance and diagnosis performance of the RBFD model based on I-PixelHop under industrial big data.