Motors are the higher energy-conversion devices that consume around 40% of the global electrical generated energy. Induction motors are the most popular motor type due to their reliability, robustness, and low cost. Therefore, both condition monitoring and fault diagnosis of induction motor faults have motivated considerable research efforts. In this paper, a comprehensive review of the recent techniques proposed in the literature for broken bar faults detection and diagnosis is presented. This paper mainly investigates the fault detection methods in line-fed and inverter-fed motors proposed after 2015 and published in most relevant journals and conferences. The introduced review has deeply discussed the main features of the reported methods and compared them in many different aspects. Finally, the study has highlighted the main issues and the gaps that require more attention from researchers in this field.

Most of the published research studies for detecting induction motor broken bar faults (BBFs) use a time-frequency (t -f) decomposition tool to characterize the fault-related components. However, the identification and the assessment of these components in (t -f) domain require skilled user or powerful pattern recognition technique. Moreover, a relatively long starting duration is necessary. This paper introduces an automated scheme to detect BBFs and distinguish fault severity in induction motors under start-up conditions regardless of the user experience and even under short starting duration and in a noisy environment. This scheme is based on the analysis of the starting current using optimized Stockwell Transform (ST). An active-set algorithm is applied to maximize the energy concentration of the left-side harmonic (LSH) component. Then, an adaptive time-frequency filter is applied to extract the LSH component from the (t -f) domain, where the energy of the right part of LSH (RLSH) is utilized as an effective index for BBFs detection and for discriminating BBFs severity. Both real experimental data and simulation-based tests on 0.746 kW and 11 kW motors are used to extensively verify the performance of the proposed scheme. The achieved results have ensured that the proposed scheme can achieve a high accuracy with the minimum data and shortest acquisition time in comparison with some recent methods in literature.

With the widespread use of variable speed drives, a robust scheme that can detect and diagnose bearing faults under fixed and variable speed conditions becomes essential for reliable operation. Unfortunately, most of the reported methods in the literature are dedicated to working under fixed speed and will face challenges under variable speed conditions. Besides, most of them require detailed bearing information that may be unavailable in the real world. Therefore, in this paper, a new scheme is proposed for bearing faults detection and diagnosis under fixed and time-varying speed conditions. The proposed scheme is based on the analysis of vibration signals using the persistence spectrum that can provide images rich with health-related features largely independent from rotating speed. Then, the produced image is compared with priorly stored images of the persistence spectrum of a healthy case. This comparison is performed using the multi-scale structural similarity index, which is a robust basis for images comparison without the need for training or expert knowledge. The obtained index is compared against an adaptive threshold for fault detection. Upon detecting a fault, the persistence spectrum image is compared with that of stored different fault types for fault diagnosis. The proposed scheme is extensively validated using three experimental datasets under different speed conditions. The results show that it can detect bearing faults in an earlier stage without the need for bearing specifications or shaft speed. Moreover, it can successfully diagnose bearing faults severity with accuracy reaching 100% with the minimum required data.

This paper introduces a novel online adaptive protection scheme to detect and diagnose broken bar faults (BBFs) in induction motors during steady-state conditions based on an analytical approach. The proposed scheme can detect precisely adjacent and non-adjacent BBFs in their incipient phases under different inertia, variable loading conditions, and noisy environments. The main idea of the proposed scheme is monitoring the variation in the phase angle of the main sideband frequency components by applying Fast Fourier Transform to only one phase of the stator current. The scheme does not need any predetermined settings but only one of the stator current signals during the commissioning phase. The threshold value is calculated adaptively to discriminate between healthy and faulty cases. Besides, an index is proposed to designate the fault severity. The performance of this scheme is verified using two simulated motors with different designs by applying the finite element method in addition to a real experimental dataset. The results show that the proposed scheme can effectively detect half, one, two, or three broken bars in adjacent/non-adjacent versions and also estimate their severity under different operating conditions and in a noisy environment, with accuracy reaching 100% independently from motor parameters.