Advances in Diagnostic Techniques for Induction Machines

By A. Bellini, F. Filippetti, C. Tassoni, Gérard-André Capolino. IEEE transactions on industrial electronics (1982. Print), 2008

1,114 citations recorded by the source corpus.

This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years. A comprehensive list of references is reported and examined, and research activities classified into four main topics: 1) electrical faults; 2) mechanical faults; 3) signal processing for analysis and monitoring; and 4) artificial intelligence and decision-making techniques.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/9af4f7e9c64b7decdc82f33b9c8692a5cd12fed7

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In Control and Systems Engineering

In Electrical and Electronic Engineering

In Mechanical Engineering

Citations in the corpus

Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics (2015, background): “Moreover, the EMD is popular in demodulation applications, whereas the WT is commonly used in vibration content characterization [20] and has better applicability [4], particularly when vibration data come from rotating machinery such as bearings [24], [25] or cutting tools [4], [26].”
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks (2016, background): “capability is increased together with the computational cost [6].”
Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis (2014, background): “According to reports [2]–[4], bearing failure is the most common failure mode of induction motors and BLDC motor fans.”
Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art (2015, background): “Although there are excellent recent review papers in this area [1], [2], the aforementioned dynamic activity has led to the appearance of very new contributions of indubitable interest.”
Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison (2008, background): “Many papers can be found in the literature concerning the general condition monitoring of induction machines [1]–[3] where the different fault types are analyzed.”
Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks (2013, background): “It is well known that the bearings represent one of the most common sources of faults in electromechanical systems [5]–[8].”
Open-Transistor Fault Diagnostics in Voltage-Source Inverters by Analyzing the Load Currents (2009, background): “By construction, Fault_Certainty(F) ∈ [0, 1 ] with 0 representing the lowest and 1 representing the highest level of”
A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains (2020, background): “Besides that, [26] and [67]–[70] provide some beneficial solutions about the frequency-domain methods.”
Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors (2011, background): “An exhaustive survey of detection and diagnosis methods of various faults such as stator faults and bearing damage in induction machines has been presented in [4] and [ 5 ].”
Cost-Effective Condition Monitoring for Wind Turbines (2010, background): “In comparison with conventional stator current analysis, widely adopted for condition monitoring motors [4], [6], power”
Induction machine faults detection using stator current parametric spectral estimation (2015, methodology): “The classical periodogram have been applied for fault detection in [11], [12].”