The limitations of the thermal, vibration, or electrical monitoring of electric machines such as false indications, low sensitivity, and difficulty of fault interpretation have recently been exposed. This has led to a shift in the direction in research toward applying new techniques for improving the reliability of condition monitoring. With the changing environment, the purpose of this article is to provide an overview of the recent trends in the industrial demand and research activity in condition monitoring technology. The new developments in insulation testing, electrical testing, flux analysis, transient analysis, and fault prognostics are summarized. The challenges and recommendations for future work for the new technologies are also explored to help support target research and development efforts according to industrial needs.

The inter-turn faults are one of the most (if not the most) challenging electrical machine failures to detect online and at incipient severity stages. Past works and experience have shown that this specific fault type will lead to a ground fault and consequently a catastrophic failure of the machine in a very small amount of time. Past works have proposed techniques and methodologies to confront this dangerous fault. However, a common feature is most past efforts is the high level of severity, limited by external resistors to avoid machine breakdowns. Scenarios like the above are not of much use in industry because they lead to the development of diagnostic techniques insensitive to the real fault severity levels that are non-catastrophic and that are by their nature very low. This is the motivation behind this work, which challenges the existing background in this field and offers a reliable solution, which may be adopted in industry. The paper studies induction motors with incipient inter-turn fault severity with many well-known techniques. The experimental results prove many methods unreliable and insensitive to low level inter-turn fault detection. Finally, the authors propose a novel method that relies on the monitoring of the stray flux at three positions of the machine.

The cracked or broken bar fault constitutes about 5-10% of total induction motor failures and leads to malfunction as well as reduction of the motor's life cycle. This is the reason why there is continuous research on techniques for prompt detection. In this study, a study of the influences of the broken bar fault to the electromagnetic characteristics of the induction motor is presented, using an asynchronous cage motor and finite element method analysis. To this direction, two models have been created and studied: a healthy and one with a broken bar. Additionally, a new approach on the detection of the broken rotor bar fault through the electromagnetic torque monitoring is suggested and validated through experimental results.

This work offers a reliable solution to the detection of broken rotor bars in induction machines with a novel methodology, which is based on the fact that the fault related harmonics will have oscillating amplitudes due to the speed ripple effect. The method consists of two main steps: Initially, a time-frequency transformation is used and the focus is given on the steady-state regime; thereupon, the fault related frequencies are handled as periodical signals over time and the classical Fast Fourier Transform is used for the evaluation of their own spectral content. This leads to the discrimination of sub-components related to the fault and evaluation of their amplitudes. The versatility of the proposed method relies on the fact that it reveals the aforementioned signatures to detect the fault, regardless of the spatial location of the broken rotor bars. Extensive finite element simulations on a 1.1MW induction motor and experimental testing on a 1.1kW induction motor lead to the conclusion that, the method can be generalized on any type of induction motor independently from the size, power, number of poles and rotor slot numbers.

Monitoring of induction motor faults based on stray flux measurement has been investigated by many researchers due to its potential benefits in cost and simplicity. Although it was shown that flux based monitoring can provide sensitive fault detection comparable to that of motor current signature analysis (MCSA), the lack of “remote” monitoring capability has limited its practical use. The performance and reliability of stray flux-based detection of induction motor rotor cage faults is evaluated in this paper. It is shown for the first time in this work that spectrum analysis of the radial stray flux can provide reliable detection of rotor faults immune to the influence of rotor axial air ducts, which is the most common cause of false rotor fault alarms. The reliability and sensitivity of stray flux based rotor fault detection is demonstrated through experimental testing on laboratory and 6.6 kV field motors.

In this paper, a new diagnostic mean is introduced for accurate, reliable, and effective diagnosis of faults in delta-connected induction motors. This mean is the zero-sequence stator current spectrum. The study is carried out with analytical calculations and finite-element method simulations. Most common faults are examined for a 4-kW cage induction motor. The analysis will reveal that the proposed mean is able to successfully identify the static eccentricity fault, even in induction motors which produce principal slot harmonics, the broken bar fault, the unbalanced voltage supply, and the stator interturn short-circuit. It will also be shown that through the proposed method, it is possible to distinguish the interturn short-circuit from the unbalanced voltage supply.