The fault diagnosis of rotating electrical machines has received an intense amount of research interest during the last 30 years. Reducing maintenance costs and preventing unscheduled downtimes, which result in losses of production and financial incomes, are the priorities of electrical drives manufacturers and operators. In fact, both correct diagnosis and early detection of incipient faults lead to fast unscheduled maintenance and short downtime for the process under consideration. They also prevent the harmful and sometimes devastating consequences of faults and failures. This topic has become far more attractive and critical as the population of electric machines has greatly increased in recent years. The total number of operating electrical machines in the world was around 16.1 billion in 2011, with a growth rate of about 50% in the last five years [1].

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Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques

Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.

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Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

An analytical technique using the `Newton-Raphson' method is presented to identify the saturated magnetising reactance and the generated frequency of a self-excited induction generator for a given capacitance, speed and load. The technique is shown to be very efficient in analysing such systems under steady state. Computed results are compared with the experimentally obtained values on a laboratory machine, and a reasonable correlation has been observed. Effects of various system parameters on the steady-state performance have been studied, and the results presented provide guidelines for optimum design of such systems.

Analysis of self-excited induction generators

This paper presents a new signature for detection of rotor faults in induction motors, such as eccentricity and broken rotor bars, that uses the external magnetic field analysis. The proposed method is based on the variations of axial flux density in the presence of these faults. The low frequency part of the magnetic field spectrum is particularly analyzed. The analysis is realized through a machine modeling based on permeance circuit under eccentricity fault and also by machine modeling based on coupled magnetic circuit theory under broken rotor bars fault. Analytical relations which describe the machine operation under broken bars fault highlight the influence of speed variation to modify the low frequency components of the external magnetic field. The theoretical results have been validated by experimental measurements. In particular, an inverse stator cage induction machine have been used to measure the bar currents under healthy and faulty cases.

Study of Rotor Faults in Induction Motors Using External Magnetic Field Analysis

Rolling bearing faults are generally slowly progressive; therefore, the development of an effective diagnostic technique could be worth detecting such faults in their incipient phase and preventing complete failure of the motor. The methods proposed in the literature for this purpose are mainly based on measuring and analyzing vibration and current. Here, a novel technique based on the stray flux measurement in different positions around the electrical machine is proposed. The main advantages of this method are due to the simplicity and the flexibility of the custom flux probe with its amplification and filtering stage. The flux probe can be easily positioned on the machines and adapted to a wide range of power levels. This paper also reports an extensive survey on the stray-flux-based fault detection methods for induction motors, prior to introducing a novel sensor/diagnostic scheme.

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

The stray flux in the vicinity of an electrical machine is inherent to its running. It is tied to the magnetic state of the machine and therefore can be affected by the presence of a fault in the machine. Accurate modeling of the external magnetic field is quite complicated and simple models are required in order to analyze the effect of an internal fault on the stray flux. This paper displays the possibilities offers by the stray flux for fault detection. A simple model of the external magnetic field is presented and a study in healthy and faulty conditions is developed. Two kind of fault in induction machine are considered: stator inter-turn short-circuit and rotor broken bar. Experimental results are in agreement with the theory, they highlight the differentiation between the radial flux and the axial flux.

Electrical machines fault diagnosis by stray flux analysis

This paper proposes a diagnosis method that exploits the information delivered by external flux sensors placed in the vicinity of rotating electrical machines, in order to detect a stator interturn short circuit. This fault induces a dissymmetry in the external magnetic field that can be measured by the sensors. Sensitive harmonics are extracted from the signals delivered by a pair of sensors placed at 180° from each other around the machine, and data obtained for several sensor positions are analyzed by fusion techniques using the belief function theory. The diagnosis method is applied on induction and synchronous machines with artificial stator faults. It will be shown that one can obtain high probability to detect the fault using the proposed fusion technique: on various series of measurements, the proposed approach has obtained a 90% detection rate on a considered machine.

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Information Fusion With Belief Functions for Detection of Interturn Short-Circuit Faults in Electrical Machines Using External Flux Sensors

This paper presents a statistical methodology for detection of inter-turn short-circuit fault in asynchronous and synchronous machines. This methodology uses a correlation coefficient obtained from external magnetic field measured in the machine vicinity. It is a noninvasive method which follows up the signals of two external flux sensors located symmetrically around the machine axis (180° spatially shifted). The principle is based on the calculation of the Pearson correlation coefficient between two signals delivered by two sensors S1 and S2 when the machine operates at different load conditions, which allows us to detect incipient faults in electrical induction and synchronous machines with a high probability of detection. Experimental tests are realized using two specific rewound machines to create inter-turn short-circuit faults with different severity levels.

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Detection of the Stator Winding Inter-Turn Faults in Asynchronous and Synchronous Machines Through the Correlation Between Harmonics of the Voltage of Two Magnetic Flux Sensors

This paper shows a new signature for detection of rotor bar faults in induction motors trough external magnetic field analysis. The proposed method is based on the variations of the axial flux density in the presence of rotor faults, particularly regarding the low frequency part of the frequency spectrum. The analysis of the external magnetic field variation in low frequency is realized through a machine modeling with permeance circuit which brings into play the permeances relating to the circulating fluxes on the machine extremities, in the presence of eccentricity. Analytical relations of the machine operating in the presence of a broken bar highlight the influence of speed variation to modify the low frequency components of the external magnetic field.

Remus Pusca

Papers

Study of Rotor Faults in Induction Motors Using External Magnetic Field Analysis

Electrical machines fault diagnosis by stray flux analysis

Information Fusion With Belief Functions for Detection of Interturn Short-Circuit Faults in Electrical Machines Using External Flux Sensors

Detection of the Stator Winding Inter-Turn Faults in Asynchronous and Synchronous Machines Through the Correlation Between Harmonics of the Voltage of Two Magnetic Flux Sensors

Eccentricity and broken rotor bars faults - Effects on the external axial field