Multi-source Heterogeneous Data Fusion Method for Pipe Gallery Condition Monitoring

In view of the exponential growth of the pipeline inspection data volume, the lack of utilization and analysis of the data, this paper proposes a method named subspace principal component analysis (SPCA) for pipe gallery condition monitoring that integrates multi-source heterogeneous data, aiming to improve the intelligent operation and maintenance level of pipe gallerys. First, interconnected distributed heterogeneous data sources are fused into a unified data set based on the JSON-based middleware method. Second, in order to reduce the complexity of condition monitoring and improve the accuracy, data with similar characteristics are assigned to the same subspace. Then, in each subspace, the principal component analysis (PCA) method is used to mine information and extract features. Furthermore, the features of each subspace are fused, and the local outlier factor (LOF) method that does not require data distribution is used to construct the condition monitoring model and analyze the running state. Finally, the effectiveness and superiority of the proposed method are illustrated by testing it on the operation and maintenance data of the pipe gallery and comparing it with the classical methods.