Raj Bharadwaj

Performance analysis of a three-phase induction motor under mixed eccentricity condition

A substantial portion of induction motor faults is eccentricity related. In practice, static as well as dynamic eccentricities happen to exist together. With this point in mind, an analytical approach to evaluate performance of a three-phase induction motor under mixed eccentric condition has been presented in this paper. Clear and step-by-step theoretical analysis, explaining completely the presence of certain harmonics in the line current spectrum in presence of eccentricity, is discussed. More importantly, it is shown for the first time that a link exists between the lowi and the high-frequency elements of these harmonics. It is also shown that these high-frequency components are not very strong in all types of machines. These results will be useful in generating rules and laws to formulate on-line tools for machine condition monitoring. Finite element results to substantiate the inductance values used in the simulation are also included. The analysis is validated by the line current spectrum of the eccentric machine obtained through simulation using modified winding function approach (MWFA) and experimentation.

Selection criteria of induction machines for speed-sensorless drive applications

Induction motors, both three and single phase, are used extensively for adjustable speed drive applications. These machines are structurally very robust and are primary source of motive power and speed control where DC machines cannot be used. For closed loop control of these machines, sensorless speed estimation is usually preferred. Among the current estimation techniques available for speed-sensorless induction motor drives, speed measurement based on rotor slot related harmonic detection in machine line current happens to be a prominent one. While these harmonics can be strong in certain kinds of machine, some other machines may exhibit very weak rotor slot harmonics that can be obscured by noise. Skewing, slot shapes and types, structural unbalances etc. also have a prominent effect on the detectability of these harmonics. The present paper attempts to investigate this problem based on the interaction of pole pairs, number of rotor bars and stator winding. Though the analysis and experimental results have been mainly provided for three phase squirrel cage induction motors, single phase and slip ring induction motors have also been addressed. Further, it has been shown that eccentricity related fault detection could also be easily accommodated with this kind of speed detection technique at no or negligible extra cost when certain motors are selected.

Mixed eccentricity in three phase induction machines: analysis, simulation and experiments

Fault diagnosis of electrical machines is gaining particular importance in view of machine downtime and revenue losses to the industry. Often, these machines run critical loads and their sudden breakdown can be catastrophic. Thus, the drive system of the motor should also have diagnostic features to predict machine faults at their very inception. Consequently, it becomes very important to have machine models and control techniques that can distinguish between the healthy and faulty condition of machines. Quite a few of these faults are eccentricity related. In practice, static as well as dynamic eccentricity, happen to exist simultaneously. With this point in mind, an analytical approach to evaluate performance of a three-phase induction motor under mixed eccentric condition has been presented in this paper. A clear relationship between the high and low frequency components reported earlier in literature has been established. The analysis is validated by the line current spectrum of the eccentric machine obtained through simulation. The effects of the number of rotor slots, machine pole numbers and load conditions; which can change the current spectrum considerably; can also be studied. The fault condition has been simulated using modified winding function approach (MWFA). Finite element results to substantiate the inductance values used in the simulation are presented. Experimental results to substantiate simulations have been included.

Study of three phase induction motors with incipient rotor cage faults under different supply conditions

The majority of rotor related faults in three-phase induction motors are due to broken bars and end rings. These faults occur primarily due to the thermal, magnetic, mechanical, environmental stresses that the rotor has to undergo during the routine usage. Faults involving several broken bars cause excessive vibration, noise and sparking during motor starting. Fabricated type rotors have more incidents of rotor bar and end ring breakage than cast rotors. On the other hand, cast rotors are more difficult to repair once they fail. Once a bar breaks; the condition of the neighboring bars also deteriorates progressively due to increased stresses. To prevent such a cumulative destructive process, the problem should be detected early, that is, when the bars are beginning to crack. This condition can be visualized as continuous increase in rotor bar resistance which increases from its nominal value to infinity when the bar is fully broken. Any experimental study to diagnose broken bar faults is costly as it causes irreversible damage to the rotor. Thus, a model based approach to simulate broken bar related faults at various degrees of severity is indeed essential. The present paper evaluates through simulation the line current spectrum of an induction motor at the incipient stage of bar breakage. The model can also be extended to multiple, full blown broken bar case. The speed and torque ripples caused by broken bars can also be studied. The rules and laws generated through such simulations can then be used in neural network based diagnostic tools. Results in case of complete broken bars are validated by finite element calculations. Experimental results with up to four bars partially broken with machine operating from balanced sinusoidal and inverter fed supply are presented. Simulation results showing that certain abnormal power supply conditions can produce broken bar like spectrum have also been included.

Neural speed filtering for sensorless induction motor drives

Abstract Effective sensorless dynamic speed estimation is desirable for both speed sensorless motor-drive applications and for on-line induction motor condition monitoring and assessment. In this paper, a sensorless neural adaptive speed filter is developed for induction motors running off a voltage source inverter. The filter is demonstrated by comparisons with experimental speed measurements and spectral speed estimates. In addition to nameplate information required for the initial set-up, the proposed neural speed filter uses only measured motor currents and voltages. Initial training of the speed filter is accomplished off-line, using rotor slot harmonic-based speed estimates. The developed speed filter is tested on data from a healthy motor and motor with 4 broken rotor bars. The resulting speed filtering accuracy compares favorably with speed estimation results reported in the literature. The study demonstrates the feasibility of neural adaptive speed filters for effective inverter-fed induction motor speed estimation, without explicit use of motor model parameters and speed measurements.

Sensorless detection of mechanical faults in electromechanical systems

Practical early fault detection and diagnosis systems must exhibit high level of detection accuracy, while exhibiting acceptably low false alarm rates. Further, it is desirable not to make use of add-on sensors, and require minimal information regarding the specific machine component parameters and design. In this paper the development and experimental demonstration of a sensorless detection and diagnosis system is presented for incipient faults in electromechanical systems, such as electric motors. The developed system uses recent developments in dynamic recurrent neural networks and wavelet signal processing. The signals utilized are only the motor currents and voltages, whereas the transient mechanical speed is estimated from these measurements using a recently developed speed filter. The effectiveness of the fault diagnosis system is demonstrated by detecting a wide range of mechanical faults at varying levels of deterioration. Furthermore, the ability of the diagnosis system to discriminate between false alarms and actual incipient failure conditions is demonstrated. Experimental test results from small machines, 2.2 kW, and large machines, 373 and 597 kW, are presented demonstrating the effectiveness of the proposed approach to scale-up with motor power rating.

Adaptive neural network-based state filter for induction motor speed estimation

Effective sensorless speed estimation is desirable for both online condition monitoring of induction motors and sensorless adjustable speed AC drive applications. In this paper, the authors present a neural network-based sensorless adaptive speed filter for induction motors. In addition to nameplate information required for the initial set-up, the proposed neural network-based speed filter uses only actual motor currents and voltages. The initial training of the filter helps in obtaining quite acceptable transient speed response from the estimation algorithm. The paper demonstrates the feasibility of adaptive speed filtering for induction motor which could be used for both diagnosis and control purposes.

Detection of induction motor faults-combining signal-based and model-based techniques

Effective detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance and improved operational efficiency of induction motors running off the power supply mains. In this paper, an empirical model-based fault diagnosis system is developed for induction motors using recurrent dynamic neural networks and multiresolution signal processing methods. In addition to nameplate information required for the initial set-up, the proposed diagnosis system uses measured motor terminal currents and voltages, and motor speed. The effectiveness of the diagnosis system is demonstrated through motor faults of electrical and mechanical origin staged in small and large motors.

Performance analysis of a three phase induction motor under mixed eccentricity condition

A substantial portion of induction motor faults is eccentricity related. In practice, static as well as dynamic eccentricity happen to exist together. With this point in mind, an analytical approach to evaluate performance of a three phase induction motor under mixed eccentric condition has been presented in this paper. Clear and step-by-step theoretical analysis, explaining completely the presence of certain harmonics in the line current spectrum in presence of eccentricity, is discussed. The effects of the number of rotor slots, machine pole numbers which influence the current spectrum considerably; can also be fully identified. These results will be useful in generating rules and laws to formulate on-line neural network based tools for machine condition monitoring. Finite element results to substantiate the inductance values used in the simulation are also included. The analysis is validated by the line current spectrum of the eccentric machine obtained through simulation using modified winding function approach (MWFA) and experimentation.

Neural speed filtering for induction motors with anomalies and incipient faults

Effective sensorless speed estimation is desirable for both on-line condition monitoring and assessment, and for efficiency calculation of induction motors running off the power supply mains. In this paper, a sensorless neural adaptive speed filter is developed for induction motors operating under normal and anomalous conditions, such as supply imbalance, as well as incipient faults, such as electrical, electromechanical, and mechanical faults. The filter is demonstrated by comparisons with experimental speed measurements and spectral speed estimates. In addition to nameplate information required for the initial setup, the proposed neural speed filter uses only measured motor terminal currents and voltages. Initial training of the speed filter is accomplished off-line, using rotor slot harmonic-based speed estimates. The developed speed filter is scalable and it has been used for speed estimation of induction motors with varying power ratings. Incremental tuning is used to further improve filter performance and reduce filter development time significantly.