Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison

By F. Immovilli, A. Bellini, R. Rubini, C. Tassoni. IEEE transactions on industry applications, 2008

324 citations recorded by the source corpus.

Mechanical imbalances and bearing faults account for a large majority of the faults in a machine, particularly for small-medium size machines. Therefore, their diagnosis is an intensively investigated field of research. Recently, many research activities were focused on the diagnosis of bearing faults by current signals. This paper compares the bearing fault detection capability obtained with the vibration and current signals. The paper contribution is the use of a simple and effective signal processing technique for both current and vibration signals, and a theoretical analysis of the physical link between faults, modeled as a torque disturbance, and current components. The focus of the paper is on the theoretical development of the correlation between torque disturbances and the amplitude of the current components, together with a review of fault models used in the literature. Another contribution is the re-creation of realistic incipient faults and their experimental validation. Radial effects are visible only in case of large failures that result in air-gap variations. Experiments are reported that confirm the proposed approach.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/dda0697b679f5cefb306ae0fb6ce8afc51accc92

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In Civil and Structural Engineering

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Citations in the corpus

Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain (2016, background): “More than 82% of fault diagnosismethodologies have beendone using vibration analysis [9, 14, 15].”
Deep Learning Based Approach for Bearing Fault Diagnosis (2017, unknown): “In addition to AE-based bearing fault diagnosis, fault detection and diagnosis based on traditional approaches have been reported [11]–[15].”
EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component (2013, background): “To tackle this problem, numerous failure detection techniques have proposed by analyzing the stator side electrical quantities; such as the current [13] or the instantaneous power factor [14].”
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, background): “Sensing modalities that have been explored include vibration [9], [10], acoustic noise [11], [12], stator current [13], [14], thermal-imaging [15], and multiple sensor fusion [16], among which vibration analysis is the most dominant.”
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “A simple NN is constructed in [44] and [90] with a single-hidden layer, and the infor-”
Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks (2013, background): “Most of the bearing monitoring schemes are focused on bearing localized defects [12], [13].”
Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, background): “Sensing modalities that have been explored include vibration [9], [10], acoustic noise [11], [12], stator current [13], [14], thermal-imaging [15], and multiple sensor fusion [16], among which vibration analysis is the most dominant.”