Important observations

Literature review indicates that thermal monitoring, vibration monitoring, and electrical monitoring, noise monitoring, torque monitoring and flux monitoring are the some important techniques of condition monitoring and fault diagnosis of electric machines. Now days, electric monitoring or current monitoring is more popular technique. In current monitoring, no additional sensors are necessary. This is because the basic electrical quantities associated with electromechanical plants such as currents and voltages are readily measured by tapping into the existing voltage and current transformers that are always installed as part of the protection system. As a result, current monitoring is non-intrusive and may even be implemented in the motor control center remotely from the motors being monitored. The Motor Current signature analysis (MCSA) and Current Park’s vector approach fall under current monitoring. MCSA is the most common form of signal analysis technique used in electric monitoring. In literature review, it has been shown that there is a relationship between the mechanical vibration of a machine and the magnitude of the stator current component at the corresponding harmonics. For increased mechanical vibrations, the

magnitude of the corresponding stator current harmonic components also increases. This is because the mechanical vibration modulates the air gap at that particular frequency. These frequency components then show up in the stator inductance, and finally in the stator current [3]. For this reason, the MCSA can be used to detect rotor and bearing faults. As the flux density in the air gap is defined as the product of the winding magneto-motive force (MMF) and the air-gap permeance, variations in either of these will cause anomalies in the flux distribution. The changes in the winding MMF mainly depend on the winding distribution. On the other hand, the air-gap permeance depends on numerous effects including stator slots, out-of-round rotors, air-gap eccentricities caused by mechanical unbalance and misalignment, and mechanical shaft vibrations caused by bearing or load faults [4]. MCSA detects changes in a machine’s permeance by examining the current signals. It uses the current spectrum of the machine for locating characteristic fault frequencies. The spectrum may be obtained using a Fast Fourier Transformation (FFT) that is performed on the signal under analysis. The fault frequencies that occur in the motor current spectra are unique for different motor faults. This method is the most commonly used method in the detection of common faults of induction motors. Some of the benefits of MCSA include [3, 4, 5, 7, 50, 58]:

a) Non-intrusive detection technique:

With the technological advances in current-measuring devices, inexpensive and easy- to-use clamp-on probes are more affordable and convenient to use for sampling current without having to disconnect the electrical circuit or to disassemble the equipment.

b) Remote sensing capability:

Current sensors can be placed anywhere on the electrical supply line without jeopardizing the signal strength and performance.

c) Safe to operate:

Since there is no physical contact between the current sensor and the motor-driven equipment, this type of monitoring technique is particularly attractive to applications where safety is of major concern.

Wavelet Transform can be used for fault diagnosis of induction motor. It works on principle that all signals can be reconstructed from sets of local signals of varying scale and amplitude, but constant shape. It is an easy and fast to implement data processing technique.

It analyses the signal at different frequency bands with different resolution by decomposing the signal into coarse approximation and detail information.

Current Park’s vector is most frequent used method in literature review applied to diagnose the common faults of induction motor. The analysis of the three-phase induction motor can be simplified using the Park transformation. The method is based on the visualization of the motor current Park’s vector representation. If this is a perfect circle the machine can be considered as healthy. If an elliptical pattern is observed for this representation, the machine is faulty. From the characteristics of the ellipse the fault's type can be established. The ellipticity increases with the severity of the fault [67-70]. From the literature cited, the following observations can be made:

(i) Condition monitoring has great significance in the business environment because there is need to improve reliability of machine and to reduce the cost of maintenance.

(ii) The major disadvantage of vibration monitoring is cost. A regular vibration sensor costs several hundred dollars. A high product cost can be incurred just by employing the necessary vibration sensors for a large number of electric machines. Another disadvantage of vibration monitoring is that it requires access to the machine. For accurate measurements, sensors should be mounted tightly on the electric machines, and expertise is required in the mounting. On other hand, there is no physical contact between the current sensor and motor-driven equipment in electric monitoring therefore electric monitoring is particularly attractive to applications where safety is of major concern. (iii)In current based fault detection, various types of faults may cause broadband changes in

power spectra of stator current. Therefore, researchers choose the signal processing as the tool for stator current based fault detection.

(iv) Investigations reveal that the fault frequencies occur in motor current spectra are unique for different motor faults.

(v) It has been a broadly accepted requirement that a diagnostic scheme should be non- invasive and capable of detecting faults accurately at low cost. Therefore, Motor Current Signature Analysis {MCSA) has become a widely used method because its monitoring parameter is a motor terminal quantity that is easily accessible.

(vi) Numerous applications of using electric monitoring in motor health monitoring have been published among the nuclear-generation, industrial, defense industries. In published work, researchers used the variety of motors of different rating to diagnose the faults. But very little work has been done to diagnose the all possible common fault of induction motor by using the motor of same rating and same signal processing technique. So, there is need to use the same type of motor and same signal processing technique to diagnose common faults of induction motor so that effectiveness of signal processing techniques can be studied.

(vii) It is observed that very few experimental studies have been published which may diagnose the single fault of induction motors with variety of signal processing techniques. Therefore, an experimental study must be conducted to diagnose the single fault with different signal processing techniques so that limitation of each signal processing technique can be studied.

(viii) The effectiveness of signal processing techniques for non-stationary signals has not been addressed appropriately in the literature. Therefore, more experiments need to be carried out with different signal processing techniques so that it may be examined which technique is best suited for non-stationary signals.