Diagnostics of bar and end-ring connector breakage faults in polyphase induction motors through a novel dual track of time-series data mining and time-stepping coupled FE-state space modeling

Povinelli, Richard J.; Bangura, J.F.; Demerdash, Nabeel A. O.; Brown, R.H.

doi:10.1109/60.986435

Cited by 53 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…4. The RG is used to quantify any changes in the area of the generated mass by TSDM to distinguish between the healthy and the faulty condition, which is presented below [13]:…”

Section: A Time Series Data Mining Methodsmentioning

confidence: 99%

Time Domain Signature Analysis of Synchronous Generator under Broken Damper Bar Fault

Ehya

Nysveen

Nilssen

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

Fault detection of electrical machines can avoid unplanned outage of the electricity generation at a power plant. Research related to a different type of faults in the synchronous generator, the broken damper bar (BDB) fault attracts less attention due to the low statistical population. However, a comprehensive condition monitoring system requires a method to diagnose this few in number faults also. Detection of BDB fault is difficult since damper bars are active only during a transient period, which could be start-up intervals or as a reaction to power system dynamics. In this paper, the BDB fault is detected during the machine start-up. The Radius of Gyration (RG) was introduced using a time series data mining (TSDM) approach that was applied to induced electromotive force of the rotor field winding due to BDB fault. Location and number of BDBs effect on the nominated feature are also studied. Index Terms-broken damper bar, electromotive force, fault detection, finite element modeling, gyration radius, salient pole synchronous generator, time series data mining.

show abstract

“…4. The RG is used to quantify any changes in the area of the generated mass by TSDM to distinguish between the healthy and the faulty condition, which is presented below [13]:…”

Section: A Time Series Data Mining Methodsmentioning

confidence: 99%

Time Domain Signature Analysis of Synchronous Generator under Broken Damper Bar Fault

Ehya

Nysveen

Nilssen

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

show abstract

“…� Time domain [36,37] � Frequency domain [2-4, 13, 14] � Time-frequency domain [22,24,34,38] In this section, statistical tools such as mean value, STD, energy, frequency domain and time-frequency domain…”

Section: Signal Processingmentioning

confidence: 99%

“…Fault detection based on unprocessed signal is a difficult task since the variation of the signal does not give meaningful information regarding the machine condition. Several signal processing tools are used to extract useful patterns inside the signals for fault detection that can be divided into three categories: Time domain [36, 37] Frequency domain [2–4, 13, 14] Time–frequency domain [22, 24, 34, 38] …”

Section: Signal Processingmentioning

confidence: 99%

Static and dynamic eccentricity fault diagnosis of large salient pole synchronous generators by means of external magnetic field

Ehya

Nysveen

Nilssen

et al. 2021

IET Electric Power Appl

View full text Add to dashboard Cite

Although synchronous generators are robust and long-lasting equipment of power plants, consistent electricity production depends on their health conditions. Static and dynamic eccentricity faults are among the prevalent faults that may have a costly effect. Although several methods have been proposed in the literature to detect static and dynamic eccentricity faults in salient pole synchronous generators (SPSGs), they are non-sensitive to a low degree of failure and require a predefined threshold to recognise the fault occurrence that may vary based on machine configuration. This article presents a detailed magnetic analysis of the SPSGs with static and dynamic eccentricity faults by focusing on the external magnetic field. The external magnetic field was measured using two search coils installed on the backside of the stator yoke. Also, advanced signal processing tools based on wavelet entropy were used to analyse the induced electromotive force (emf) in search coils to extract the fault index. The proposed index required no threshold to recognise the starting point of fault occurrence and was sensitive to a low degree of fault. It was also non-sensitive to load variation and noise that may induce a false alarm. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…Current signals are simulated for a 1.2‐hp, 208‐V, 60‐Hz, 4‐pole, 3‐phase squirrel cage induction motor at rated load conditions of 5% slip using the Time Stepping Coupled Finite Element‐State Space (TSCFE‐SS) method . The dataset of simulated current signals can be downloaded from the University of California, Riverside (UCR) time series data mining archive .…”

Section: Performance Evaluation On Simulated Datamentioning

confidence: 99%

Rotor fault diagnosis in induction motors by the matrix pencil method and support vector machine

Chahine

2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper proposes a high‐resolution motor‐current‐signature‐analysis method and a supervised learning algorithm for the detection and classification of broken rotor bars and broken end‐ring connectors in 3‐phase induction motors. Signature analysis relies on the matrix pencil method (MPM), a well‐known model‐based parameter estimation technique, to extract representative features or signatures from stator current signals. Extracted feature vectors are subsequently used to train off‐line a support vector machine classifier. Once trained, the classifier is tested on a benchmark dataset of simulated stator current signals representing healthy and faulty rotors with the aim of classifying the underlying motor condition. The obtained results validate the matrix pencil method as a feature extraction method and show that the trained classifier achieves a 100% success rate in identifying the number of broken bars and connectors. Moreover, the advantages of the matrix pencil method over fast Fourier transform are demonstrated using experimental data.

show abstract

Diagnostics of bar and end-ring connector breakage faults in polyphase induction motors through a novel dual track of time-series data mining and time-stepping coupled FE-state space modeling

Cited by 53 publications

References 11 publications

Time Domain Signature Analysis of Synchronous Generator under Broken Damper Bar Fault

Time Domain Signature Analysis of Synchronous Generator under Broken Damper Bar Fault

Static and dynamic eccentricity fault diagnosis of large salient pole synchronous generators by means of external magnetic field

Rotor fault diagnosis in induction motors by the matrix pencil method and support vector machine

Contact Info

Product

Resources

About