A data-driven multiplicative fault diagnosis approach for automation processes

- A new data-driven KPI monitoring and diagnosis technique for multiplicative faults/malfunctions in industrial processes.
- Clear interpretation of the basic idea, low design efforts and easy implementation.
- Monte-Carlo simulation is performed to demonstrate the effectiveness from statistical aspect.
- Case study on Tennessee Eastman (TE) (closed-loop) benchmark process to show its practical applicability.

This paper presents a new data-driven method for diagnosing multiplicative key performance degradation in automation processes. Different from the well-established additive fault diagnosis approaches, the proposed method aims at identifying those low-level components which increase the variability of process variables and cause performance degradation. Based on process data, features of multiplicative fault are extracted. To identify the root cause, the impact of fault on each process variable is evaluated in the sense of contribution to performance degradation. Then, a numerical example is used to illustrate the functionalities of the method and Monte-Carlo simulation is performed to demonstrate the effectiveness from the statistical viewpoint. Finally, to show the practical applicability, a case study on the Tennessee Eastman process is presented.