Concurrent Projection to Latent Structures for Output-relevant and Input-relevant Fault Monitoring

When process faults occur, the process condition changes which is reflected in process variables. If these abnormal variations are not properly annihilated in the process, poor product quality occurs as a consequence. This paper proposes a new concurrent projection to latent structures for the monitoring of output-relevant faults that affect the quality and input-relevant process faults that should be alarmed as well. The input and output data spaces are concurrently projected to five subspaces, a joint input-output subspace that captures covariations between input and output, an outputprincipal subspace, an output-residual subspace, an inputprincipal subspace, and an input-residual subspace. Process fault detection indices are developed based on the partition of subspaces for various types of fault detection alarms. The proposed monitoring method offers complete monitoring of faults that happen in the predictable output subspace and the unpredictable output residual subspace, as well as faults that affect the input spaces and could be incipient for the output. Numerical simulation examples are given to illustrate the effectiveness of the proposed methods.