Diagnosis of Cascade Control Loop Status Using Performance Analysis Based Approach

On the basis of the achievable performance of the control outputs, fault diagnosis of two cascade control systems, including series cascade control (SCC) and parallel cascade control (PCC), is developed. Without any prior knowledge of complicated operating processes and/or external inputs perturbing the operating system, the accurate fault identification can be achieved by a series of the statistical hypothesis procedures applied to the currently measured data. To isolate possible faults, the output variances of the primary and secondary loops are separated into cascade-invariant (CI) and cascade-dependent (CD) terms, respectively, by the Diophantine decompositions. After a sequence of the hypotheses tests is performed on the CI and the CD terms of current control and the achievable performance conditions, the hierarchical diagnosis trees for the primary and secondary outputs are established, respectively, to explore possible faults. The final faults can be inferred by merging both diagnosis trees. The statistical inference systems for SCC and PCC structures are the same. The performance of the proposed method is demonstrated via two examples, including a simulation case with a single fault and a pilot-level tank system with multiple faults.