Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis

Abstract

Induction motors (IMs) play an essential role in the field of various industrial applications. Long-time service and tough working situations make IMs become prone to a broken rotor bar (BRB) that is one of the major causes of IMs faults. Hence, the continuous condition monitoring of BRB faults demands a computationally efficient and accurate signal diagnosis technique. The advantage of high reliability and wide applicability in condition monitoring and fault diagnosis based on vibration signature analysis results in an improved cyclic modulation spectrum (CMS), which is one of the cyclic spectral analysis algorithms. CMS is proposed in this paper for the detection and identification of BRB faults in IMs at a steady-state operation based on a vibration signature analysis. The application of CMS is based on the short-time Fourier transform (STFT) and the improved CMS approach is attributed to the optimization of STFT. The optimal window is selected to improve the accuracy for identifying the BRB fault types and severities. The appropriate window length and step size are optimized based on the selected window function to receive a better calculation benefit through simulation and experimental analysis. Compared to other estimators, the improved CMS method provides better fault detectability results by analyzing vertical vibration signatures of a healthy motor, and damaged motors with 1 BRB and 2 BRBs under 0%, 20%, 40%, 60%, and 80% load conditions. Both synthetic and experimental investigations demonstrate the proposed methodology can significantly reduce computational costs and identify the BRB fault types and severities effectively.

Automatic Early Broken-Rotor-Bar Detection and Classification Using Otsu Segmentation

“…this work, for early detection and classification of BRB in IM, regarding percentage load torque, processing time and classification effectiveness. Taking into account this comparison, the proposed methodology shows high accuracy in the detection and classification of broken rotor bars, reaching at least the same sensitivity and effectiveness than others approaches; although, it has been extensively proven that the higher the fault severity and/or the mechanical load, the easier the BRB detection [26,27], [36][37][38][39][40][41]. Furthermore, it is desirable to have a visual representation of the faultfrequency evolution in time, to corroborate the diagnosis of the induction motor operational condition.…”

New Spectral Markers for Broken Bars Diagnostics in Induction Motors

“…Wang et al used involving cyclic modulation spectral analysis to detect broken rotor bars, which appear to be one of the most common causes of inductive motors faults [5]. The given method is based on vertical vibration signature analysis resulting in an improved cyclic modulation spectrum (CMS).…”

“…Tang et al [18] discovered these harmonics but did not provide any mathematical generalization of them. Li [19] and Wang [5] analyzed only the low (0-50 Hz) frequencies. The great variety of abovementioned theories provides us the basis for this study.…”

Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors

“…Therefore, motor current signal analysis (MCSA) is widely used to detect BRB faults based on modulation characteristic analysis [3]. With the advantages of non-invasion, low cost, and the ability to operate without needing any extra sensors installed, MCSA has successfully attracted researchers' attention for detecting BRB faults with various kinds of measurements, such as vibration, acoustic, and temperature, et al [4,5]. Furthermore, various signal processing methods have been developed and investigated for motor current signatures analysis [6,7].…”