Parameter estimation for fault detection and classification in centrifugal pumps

Fault detection and diagnosis, have been topics of great interest in both academia and industry. A well-designed strategy can considerably save operating costs by minimizing services downtime and replaceable items costs. A good knowledge of the operating machine's dynamics, a good measurements technique can significantly add value to the strategy design. Different approaches are nowadays used to effectively detect and diagnose faults. They can be summarized as data-driven, analytically based, and knowledge-based methods. This paper discussed a parametric approach on a steady-state for fault detection on pumps. The parametric estimation method requires good modeling of the system dynamics and proper use of the parameters as features for designing a diagnosis algorithm. The paper demonstrates that the correlation induced by the variation between the head and torque could be easily detected. The paper also suggests using the likelihood ratio test, in order to discretize parameters likely to hold direct information on the fault.