Franz Pirker

Sequences of field oriented control for the detection of faulty rotor bars in induction machines. The Vienna monitoring method

Rotor cage asymmetries of induction machines cause disturbances of the air-gap flux pattern. These deviations affect torque and speed as well as stator terminal voltages and currents. The proposed fault detection technique senses the actual machine state with the help of real-time space-phasor models. The Vienna Monitoring Method compares online a voltage model output with a current model and observes the deviations in a rotor fixed reference frame. High accuracy and robustness allow the detection of a faulty rotor bar out of the switched voltage and current signals of an inverter-fed machine in an early state. The focus of this paper is the detection of a single rotor bar increase under transient speed conditions without the necessity of a clutched load. During an inverter-controlled acceleration, lasting only 200 ms, the Vienna Monitoring Method evaluates currents, voltages, and rotor position for the calculation of an indication quantity that allows for reliable detection. As only one acceleration task does not excite every rotor cage bar sufficiently, a set of acceleration and deceleration cycles has to be driven.

Rotor temperature estimation of squirrel-cage induction motors by means of a combined scheme of parameter estimation and a thermal equivalent model

This paper deals with a rotor temperature estimation scheme for fan-cooled mains-fed squirrel-cage induction motors. The proposed technique combines a rotor resistance estimation method with a thermal equivalent circuit. Usually, rotor resistance estimation works quite well under rated load conditions. By contrast, if the motor is slightly loaded, rotor resistance estimation becomes inaccurate due to the small slip. Therefore, rotor temperature estimation under low-load conditions may be estimated by a thermal equivalent model. In order to determine the rotor resistance and, thus, rotor temperature accurately, several machine parameters have to be obtained in advance. Load tests provide the leakage reactance and the iron losses of the induct machine. The stator resistance has to be measured separately. The parameters of the thermal equivalent model are a thermal resistance and a thermal capacitance. These parameters are derived from a heating test, where the reference temperature is provided from the parameter model in the time domain. This lumped thermal parameter model is based on the assumption that the total rotor temperature increase is caused by the total sum of the losses in the induction machine. Measuring results of a 1.5-kW and an 18.5-kW four-pole low-voltage motor and a 210-kW four-pole high-voltage motor are presented and compared.

On-line rotor cage monitoring of inverter-fed induction machines by means of an improved method

This paper suggests a fault-detection technique to monitor defects such as cracked rotor bars in induction machines. It has been introduced as the Vienna monitoring method. Rotor bar faults cause an asymmetric magnetic flux pattern in the air gap. Thus, the current phasor (or voltage phasor at current-controlled machines), flux phasor and air-gap torque differ from those of an ideal symmetric machine. The Vienna monitoring method compares the outputs of a reference model, which represents an ideal machine, to a measurement model. Observing the deviations of these two models makes it possible to detect and even locate rotor faults. It can be applied to inverter-fed machines as no frequency analysis is used. The method is verified by online experimental results from a DSP-controlled IGBT inverter drive. The findings match the outcomes of a detailed machine simulation. Air-gap flux density evaluation by a measurement coil system proves both the excellent sensitivity and fault location ability of the proposed scheme.

Model-based detection of rotor faults without rotor position sensor-the sensorless Vienna monitoring method

This paper deals with an extended approach of the Vienna Monitoring Method (VMM), which is a model-based technique to detect rotor asymmetries in the squirrel cage of an induction machine. The conventional VMM requires the measured voltages, currents, and the signal of a rotor position sensor. The novel scheme presented in this paper alternatively works without a rotor position sensor. In particular, for low-inertia drives, accurate estimation of rotor position is required. The rotor-fault-related double-slip-frequency torque modulation causes a speed ripple with the same frequency. Consequently, low-inertia drives are exposed to higher speed ripples. In this case, it is not sufficient to estimate the mean value of the actual speed, only. Even the speed ripple has to be acquired to benefit from the accuracy of the employed models of the VMM. The proposed technique evaluates the signatures of an inherent rotor fault in order to determine the rotor position signal. The speed ripple can be obtained from the torque modulations that the models already compute. This way, an accurate rotor fault detection technique without rotor position sensor can be realized.

Robust Rotor Fault Detection by Means of the Vienna Monitoring Method and a Parameter Tracking Technique

The Vienna monitoring method (VMM) is a model-based rotor fault detection method that utilizes the voltage and current models for the computation of a fault indicator. So far, the VMM was investigated with fixed rotor parameters only. In this paper, the parameters of the current model are provided by a parameter tracking technique. For this advanced rotor fault detection method, measurement results are presented for steady-state and varying load torque operations.

The Impact of Inertia on Rotor Fault Effects—Theoretical Aspects of the ViennaMonitoring Method

This paper investigates the impact of inertia on the fault signatures of squirrel cage induction machines. Rotor faults give rise to harmonic sideband currents and double slip frequency shaft torque oscillations, however. The total drive inertia thus influences the speed ripple which, again, influences the balance of the magnitudes of the lower and upper sideband current. Considering certain assumptions, the impact of the speed ripple on the current harmonics with respect to the stator and rotor fixed reference frame is investigated analytically. Additionally, the derived equations are applied to the Vienna Monitoring Method, a model-based rotor fault detection method.

Model based detection of rotor faults without rotor position sensor-the sensorless Vienna monitoring method

This contribution deals with an extended approach of the Vienna monitoring method (VMM), which is a model based technique to detect rotor asymmetries in the squirrel cage of an induction machine. The conventional VMM requires the measured voltages, currents and the signal of a rotor position sensor. The novel scheme presented in this paper alternatively works without a rotor position sensor. Especially for low inertia drives accurate estimation of rotor position is required. The rotor fault related double slip frequency torque modulation causes a speed ripple with the same frequency. Consequently, low inertia drives are exposed to higher speed ripples. In this case, its not sufficient to estimate the mean value of the actual speed, only. Even the speed ripple has to be acquired to benefit from the accuracy of the employed models of the VMM. The proposed technique evaluates the signatures of an inherent rotor fault in order to determine the rotor position signal. The speed ripple can be obtained from the torque modulations that the models already compute. This way, an accurate rotor fault detection technique without rotor position sensor can be realized.

The integration of machine fault detection into an indirect field oriented induction machine drive control scheme-the Vienna Monitoring Method

This paper proposes a new approach for an on-line induction machine monitoring scheme tailored for variable speed drives. As the application of the commonly employed spectrum analysis techniques is complicated by the inverter drives inherent frequency variation, the Vienna Monitoring Method avoids any frequency analysis and observes instead the machine state with the help of on-line models. The key for the fault detection is thereby the comparison of the estimated machine states out of two independent machine models with different model structures. While the models respond identically to the regular machine operation they diverge in case of a structural machine asymmetry. Experimental results from an IGBT drive show that a single rotor bar resistance change can be detected unequivocally. The method is thus sensitive enough to detect an upcoming fault in a very early state. As the extent of the cage asymmetry is estimated too, maintenance or repair can be conveniently scheduled on time.

An Advanced Simulation Tool Based on Physical Modelling of Electric Drives in Automotive Applications

In this paper a comparison of two different modelled permanent magnet synchronous machines will be shown. A simulation of a vehicle concept with both models will be described and investigated by means of an advanced simulation tool - the SmartElectricDrives (SED) Library - and a Modelica/Dymola simulation environment. The SED library provides the most important electric drive types including controlled electric machines which are described by algebraic and differential equations. Due to this kind of modelling, the machines provide higher flexibility in case of parameter variation and a more realistic behaviour. With the aim of achieving the highest efficiency possible for the automotive drive, which means finding an optimal setting regarding fuel and power consumption, it is necessary to simulate the entire drive train of the vehicle. This includes the internal combustion engine (ICE) as well as electric traction machines or additional electric auxiliary drives. These vehicle simulations may be performed by the car manufacturer to obtain specifications for the vehicle components to be constructed by fulfilling the given boundary conditions like electric machines speed, power and torque and therefore the resulting currents and voltages for the optimal dimensioning of each component.

The Impact of Inertia on Rotor Faults Effects - Theoretical Aspects of the Vienna Monitoring Method

This paper investigates the impact of inertia on the fault signatures of squirrel cage induction machines. Rotor faults give rise to harmonic side band currents and double slip frequency shaft torque oscillations, however. The total drive inertia thus influences the speed ripple which, again, influences the balance of the magnitudes of the lower and upper side band current. Considering certain assumptions, the impact of the speed ripple on the current harmonics with respect to the stator and rotor fixed reference frame is investigated analytically. Additionally, the derived equations are applied to the Vienna Monitoring Method, a model based rotor fault detection method.