Comprehensive subspace decomposition and isolation of principal reconstruction directions for online fault diagnosis

• A relative principal component of fault reconstruction (RPCFR) modeling algorithm is proposed in the present work for online fault diagnosis.
• The algorithm gives the original fault space a further decomposition according to their relationships with the fault-free process information.
• Fault reconstruction model is developed only using those fault directions that can best characterize the effect of the fault deviations relative to normal data.
• The proposed method provides comprehensive analysis of fault information from the reconstruction perspective, which can improve the diagnosis efficiency.

Reconstruction based fault diagnosis isolates the fault cause by finding fault subspace to bring the faulty data back to normal. However, the conventional reconstruction model was often defined using principal component analysis (PCA) to extract the general distribution information of fault data and may not well discriminate fault from normal status. It thus may fail to recover the fault-free data efficiently. To overcome the above problem, a relative principal component of fault reconstruction (RPCFR) modeling algorithm is proposed in the present work for fault subspace extraction and online fault diagnosis. Instead of directly modeling fault data to extract the reconstruction directions, the algorithm gives the original fault space a comprehensive decomposition according to its relationship with the normal process information. Those fault directions that can more efficiently characterize the effects of fault deviations relative to normal data are separated from the others and used for fault reconstruction. Its performance on online fault diagnosis is illustrated by the data from the Tennessee Eastman process.