C. Tassoni

Advances in Diagnostic Techniques for Induction Machines

This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years. A comprehensive list of references is reported and examined, and research activities classified into four main topics: 1) electrical faults; 2) mechanical faults; 3) signal processing for analysis and monitoring; and 4) artificial intelligence and decision-making techniques.

Recent developments of induction motor drives fault diagnosis using AI techniques

This paper presents a review of the developments in the field of diagnosis of electrical machines and drives based on artificial intelligence (AI). It covers the application of expert systems, artificial neural networks (ANNs), and fuzzy logic systems that can be integrated into each other and also with more traditional techniques. The application of genetic algorithms is considered as well. In general, a diagnostic procedure starts from a fault tree developed on the basis of the physical behavior of the electrical system under consideration. In this phase, the knowledge of well-tested models able to simulate the electrical machine in different fault conditions is fundamental to obtain the patterns characterizing the faults. The fault tree navigation performed by an expert system inference engine leads to the choice of suitable diagnostic indexes, referred to a particular fault, and relevant to build an input data set for specific AI (NNs, fuzzy logic, or neuro-fuzzy) systems. The discussed methodologies, that play a general role in the diagnostic field, are applied to an induction machine, utilizing as input signals the instantaneous voltages and currents. In addition, the supply converter is also considered to incorporate in the diagnostic procedure the most typical failures of power electronic components. A brief description of the various AI techniques is also given; this highlights the advantages and the limitations of using AI techniques. Some applications examples are also discussed and areas for future research are also indicated.

Quantitative evaluation of induction motor broken bars by means of electrical signature analysis

The paper reports the comparison and performance evaluation of different diagnostic procedures that use input electric signals to detect and quantify rotor breakage in induction machines supplied by the mains. Besides the traditional current signature analysis based on one-phase current spectrum lines at frequencies (1/spl plusmn/2s)f, the procedures based on analysis of the line at frequency 2sf in the spectrum respectively of electromagnetic torque, space vector current modulus and instantaneous power are considered. These last procedures have similar features and the comparison is developed on the basis of instantaneous torque. It is shown that the speed ripple introduces two further terms in the instantaneous torque, decreasing the accuracy of the diagnosis. It is shown that there is a link between the angular displacement of the current sideband components at frequencies (1/spl plusmn/2s)f. This allows a more correct quantitative evaluation of the fault and to show the superiority of the sideband current components diagnostic procedure over the other proposed methods.

Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison

Early diagnosis of faults in induction machines is an extensively investigated field, for cost and maintenance savings. Mechanical imbalances and bearing faults account for a large majority of faults in a machine, especially for small-medium size machines. Therefore their diagnosis is an intensively investigated field or research. Recently many research activities were focused on the diagnosis of bearing faults by current signal. Stator current components are generated at predictable frequencies related to the electrical supply and mechanical frequencies of bearing faults. However their detection is not always reliable, since the amplitude of fault signatures in the current signal is very low. This paper compares the bearing fault detection capability obtained with vibration and current signals. To this aim a testbed is realized that allows to test vibration and current signal on a machine with healthy or faulty bearings. Signal processing techniques for both cases are reviewed and compared in order to show which procedure is best suited to the different type of bearing faults. The paper contribution is the use of a simple and effective signal processing technique for both current and vibration signals, and a theoretical analysis of the physical link between faults and current components including torque ripple effects. As expected because of the different nature of vibration and current, bearing fault diagnosis is effective only for those fault whose mechanical frequency rate is quite low. Experiments are reported that confirm the proposed approach.

On-field experience with online diagnosis of large induction motors cage failures using MCSA

The experience gained by ENEL Produzione (previously the Italian Electric Board) on monitoring the cage condition of large induction motors is reported in this paper. The diagnostic procedure is based on the motor current signature analysis and, in particular, on the two sideband current components near the frequency fundamental line that appear in the current power spectrum when a rotor bar/ring breakage occurs. According to the developed procedure, a diagnostic index obtained from these components is stored and its trend as a function of time allows for the detection of the occurrence of a failure in most cases. This event is clearly shown by the overcoming of a prefixed and triggered threshold. Moreover, machines with particular rotor magnetic structure are considered. In this case, unexpectedly high sideband components appear, even in the presence of healthy cages, and the test procedure was adapted to account for these conditions.

Neural networks aided on-line diagnostics of induction motor rotor faults

An improvement of induction-machine rotor fault diagnosis based on a neural network approach is presented. A neural network can replace in a more effective way the faulted machine models used to formalize the knowledge base of the diagnostic system with suitably chosen inputs and outputs. Training the neural network by data obtained from experimental tests on healthy machines and from simulation in the case of faulted machines, the diagnostic system can discern between healthy and faulty machines. This procedure replaces the formulation of a trigger threshold, required in the diagnostic procedure based on the machine models.<<ETX>>

Diagnosis of Bearing Faults of Induction Machines by Vibration or Current Signals: A Critical Comparison

Mechanical imbalances and bearing faults account for a large majority of the faults in a machine, particularly for small-medium size machines. Therefore, their diagnosis is an intensively investigated field of research. Recently, many research activities were focused on the diagnosis of bearing faults by current signals. This paper compares the bearing fault detection capability obtained with the vibration and current signals. The paper contribution is the use of a simple and effective signal processing technique for both current and vibration signals, and a theoretical analysis of the physical link between faults, modeled as a torque disturbance, and current components. The focus of the paper is on the theoretical development of the correlation between torque disturbances and the amplitude of the current components, together with a review of fault models used in the literature. Another contribution is the re-creation of realistic incipient faults and their experimental validation. Radial effects are visible only in case of large failures that result in air-gap variations. Experiments are reported that confirm the proposed approach.

Development of expert system knowledge base to on-line diagnosis of rotor electrical faults of induction motors

The authors consider the development of a knowledge base branch related to rotor electrical faults in squirrel cage machines, to be implemented in an expert system (ES), utilizing instantaneous values as input data. The knowledge base is organized in two levels: in the first level diagnostic indexes for the orientation of the ES inference engine toward the appropriate branch of the fault tree are utilized. The second level includes the deep knowledge with a data set obtained on the basis of a complete faulty machine model. The diagnostic indexes of the first level concern how to distinguish faulty events from the healthy signals due to the unavoidable manufacturing asymmetries. They are pointed out through a simplified model of a faulted rotor that needs few machine parameters. Some diagnosis examples are reported to describe the sequence of operations of the diagnostic system.<<ETX>>

Monitoring of induction motor load by neural network techniques

This paper deals with the electric tracing of the load variation of an induction machine supplied by the mains. A load problem, like a torque dip, affects the machine supply current and consequently it should be possible to use the current pattern to detect features of the torque pattern, using the machine itself as a torque sensor. But current signature depends on many phenomena and misunderstandings are possible. At first the effect of different load anomalies on current spectrum, in comparison with other machine problems like rotor asymmetries, are investigated. Reference is made to low frequency torque disturbances, which cause a quasistationary machine behavior. Simplified relationships, validated by simulation results and by experimental results, are developed to address the current spectrum features. In order to detect on-line anomalies, a current signature extraction is performed by the time-frequency spectrum approach. This method allows the detection of random faults as well. Finally it is shown that a neural network approach can help the torque pattern recognition, improving the interpretation of machine anomalies effects.

Broken bar detection in induction machines: comparison between current spectrum approach and parameter estimation approach

In this paper the diagnosis of induction machine rotor electrical faults is considered. Two approaches are compared: the current spectrum analysis and the apparent rotor resistance estimation. For the first approach the authors have developed several procedures based on different fault models of the machine. Their experience is used to approach the parameter estimation method from a theoretical point of view: the resistance variation of the balanced per-phase model is computed using the faulted machine model. It is possible to obtain, by the simulation, the expected resistance variation when some bars break in a specific machine. Moreover the numerical results can be generalized. Using a simplified model of a faulted machine a relationship is obtained, which correlates the apparent resistance variation with the number of broken bars. This relationship needs several assumptions and therefore it is an approximate one, but can be used to define the threshold level for the apparent resistance variation expected in the case of one broken bar. By this relationship it is possible to have an indication on the sensitivity of the parameter estimation approach. The superiority of the current spectrum approach over the parameter estimation approach is shown.<<ETX>>