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The number of rotors running in active magnetic bearings (AMBs) has increased over the last few years. These systems offer a great variety of advantages compared to conventional systems. The aim of this article is to use the AMBs together with a developed built-in software for identification, fault detection, and diagnosis in a centrifugal pump. A single-stage pump representing the turbomachines is investigated. During full operation of the pump, the AMBs are used as actuators to generate defined motions respectively forces as well as very precise sensor elements for the contactless measurement of the responding displacements and forces. In the linear case, meaning small motions around an operating point, it is possible to derive compliance frequency response functions from the acquired data. Based on these functions, a model-based fault detection and diagnosis is developed which facilitates the detection of faults compared to state-of-the-art diagnostic tools which are only based on the measurement of the systems outputs, i.e., displacements. In this article, the different steps of the modelbased diagnosis, which are modeling, generation of significant features, respectively symptoms, fault detection, and the diagnosis procedure itself are presented and in particular, it is shown how an exemplary fault is detected and identified.order to satisfy these requirements an integrated failure detection and diagnosis becomes increasingly important for these machines.The demand on using magnetic bearings in turbomachines has strongly increased over the last five years. This is stated in several recent publications such as Gopalakrishan (1999), Hergt (1999), and in the proceeding of the last magnetic bearing conference (ISMB, 2000). Most importantly here are the active magnetic bearings (AMBs), a typical mechatronic system. Rotors in AMBs already offer a variety of advantages compared to conventional systems. Some of them are the tuning possibilities for stiffness and damping, the absence of wear, the reduction of friction, the high running speeds, and possible unbalance compensation. In various applications the feasibility and profitability of using AMBs in turbomachines have been demonstrated, e.g., Allaire et al. (1989) and McGinnis et al. (1990). However, there is much more potential in such systems than using them as a simple bearing. AMBs also have to be used as sensor and actuator elements. They work in this new generation of turbomachines as an integrated identification and diagnosis tool. In this way it will be possible to design new machines with higher performance, higher reliability, and longer lifetimes.Equation (1) shows the linear description of the dynamic behavior of a rotor with stiffness, damping, and inertia characteristics, expressed by the matrices M, D and K. We assume that the rotor matrices are time-invariant but depend on the running speed and the actual operating condition. Mẍ(t) + Dẋ(t) + Kx(t) = f(t)[1]The forces f(t) are considered as the system inputs and the displacements x(t) as the system...

The number of rotors running in active magnetic bearings (AMBs) has increased over the last few years. These systems offer a great variety of advantages compared to conventional systems. The aim of this article is to use the AMBs together with a developed built-in software for identification, fault detection, and diagnosis in a centrifugal pump. A single-stage pump representing the turbomachines is investigated. During full operation of the pump, the AMBs are used as actuators to generate defined motions respectively forces as well as very precise sensor elements for the contactless measurement of the responding displacements and forces. In the linear case, meaning small motions around an operating point, it is possible to derive compliance frequency response functions from the acquired data. Based on these functions, a model-based fault detection and diagnosis is developed which facilitates the detection of faults compared to state-of-the-art diagnostic tools which are only based on the measurement of the systems outputs, i.e., displacements. In this article, the different steps of the modelbased diagnosis, which are modeling, generation of significant features, respectively symptoms, fault detection, and the diagnosis procedure itself are presented and in particular, it is shown how an exemplary fault is detected and identified.