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].

/pdf/trends-in-fault-diagnosis-for-electrical-machines-a-review-47iy1hp1hg.pdf

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.

/pdf/advances-in-electrical-machine-power-electronic-and-drive-4jt41t4f8x.pdf

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

The presence of electrical and mechanical faults in the induction motors (IMs) can be detected by analysis of the stator current spectrum. However, when an IM is fed by a frequency converter, the spectral analysis of stator current signal becomes difficult. For this reason, the monitoring must depend on multiple signatures in order to reduce the effect of harmonic disturbance on the motor-phase current. The aim of this paper is the description of a new approach for fault detection and diagnosis of IMs using signal-based method. It is based on signal processing and an unsupervised classification technique called the artificial ant clustering. The proposed approach is tested on a squirrel-cage IM of 5.5 kW in order to detect broken rotor bars and bearing failure at different load levels. The experimental results prove the efficiency of our approach compared with supervised classification methods in condition monitoring of electrical machines.

Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique

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.

/pdf/information-fusion-with-belief-functions-for-detection-of-3m35nirpbx.pdf

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

This paper concerns a coupled analysis of the stray magnetic field and the external housing vibration of a synchronous machine to detect rotor short-circuit faults. Indeed, in addition to the possibility to measure these parameters with non-invasive equipment, these two measures depend on the air-gap flux density that is directly impacted by the short circuits of any nature. Hence, correlating these two quantities, it is achievable to confirm or discard an eventual motor fault. This method allows both to develop an analysis of non-invasive manner and to establish the magnetic state of a synchronous generator in normal operation. The experimental survey of stray magnetic field and housing vibration spectrum is shown as a promising alternative at low cost and easy to implement and to determine, for instance, rotor turn-to-turn winding faults. Faulty and healthy signatures are drawn from the measured spectra to determine a degree of healthy state of the synchronous machine.

Non-Invasive Detection of Rotor Short-Circuit Fault in Synchronous Machines by Analysis of Stray Magnetic Field and Frame Vibrations

This paper presents a method for detecting faults in the induction machine using a flux sensor which measures the external radial leakage magnetic field of the machine. The procedure uses the spectral lines that correspond to the slotting effect which causes harmonics in the machine air gap. These harmonic frequencies, which are about a few kHz occur in a part of the spectra which is not very dense compared to the one that corresponds to the low frequencies due to the supply or the rotor revolution speed. The considered components can also be detected in the radial magnetic field flowing out of the machine. It can be shown that specific faults lead to additional spectral lines which appear clearly near the original ones due to the slotting effect. Two faults, which can be detected with the same magnetic flux sensor, are presented in this paper: the first one corresponds to a broken rotor bar, the second one is a stator inter turn short circuit.

Raphael Romary

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

Non-Invasive Detection of Rotor Short-Circuit Fault in Synchronous Machines by Analysis of Stray Magnetic Field and Frame Vibrations

Induction machine fault diagnosis using an external radial flux sensor