Incipient fault detection in induction machine stator-winding using a fuzzy-Bayesian change point detection approach

D’Angelo, Marcos Flávio Silveira Vasconcelos; Palhares, Reinaldo M.; Takahashi, Ricardo H. C.; Loschi, Rosangela H.; Baccarini, Lane Maria Rabelo; Caminhas, Walmir M.

doi:10.1016/j.asoc.2009.11.008

Cited by 51 publications

(24 citation statements)

References 31 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This model does not use coordinate transformations and is based on the voltage equations driven for every stator and rotor phase. The shortcoming of this model same as the qd model, is neglecting the core saturation and spatial harmonics [20][21][22][23].…”

Section: Introductionmentioning

confidence: 98%

Analysis of rotor and stator winding inter-turn faults in WRIM using simulated MEC model and experimental results

Roshanfekr

Jalilian

2015

Electric Power Systems Research

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 98%

Analysis of rotor and stator winding inter-turn faults in WRIM using simulated MEC model and experimental results

Roshanfekr

Jalilian

2015

Electric Power Systems Research

View full text Add to dashboard Cite

“…They rely only on acquired data during plant operation. Some examples of history based methods are neuro-fuzzy models [4], [5], neural networks [6], immune systems [7], [8], statistical methods [9], [10] and expert systems [11], [12].…”

Section: Introductionmentioning

confidence: 99%

A batch-incremental process fault detection and diagnosis using mixtures of probablistic PCA

Nakamura

Lemos

2014

2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

View full text Add to dashboard Cite

In process engineering, a fast and efficient fault detection and diagnosis (FDD) system is an essential component to improve both safety and productivity losses under abnormal conditions. Over the years, techniques based on models derived from process historical data, specially under a probabilistic framework, have gain a lot of attention. In this paper, probabilistic principal component analysis (PPCA) mixture models are used to cope with the FDD task. A batch-incremental method is proposed for statistical process monitoring, seeking to detect and learn new faulty behaviour, or yet, diagnose an already known fault. The proposed methodology was applied to the Tennessee Eastman Process under a closed-loop control, and it has shown robust and reliable results.

show abstract

“…This procedure allowed the checking if the short circuit tests had damaged the induction machine insulation system. Figure 4 shows the current fundamental component normalized in R 3 space. The same way, for the model simulation, Figure 2, we can see from this figure the two classes splitting difficulty, i.e., no fault condition or short circuit condition between turns.…”

Section: Implementation Using Experimental Datamentioning

confidence: 99%

“…Another point is the fact that many processes are in continuous activities or in aggressive environments; the motor requires monitoring since it drives a non-stop machine. It depends on a non-invasive way to detect faults, and no human is exposed directly to the machine in working condition [2,3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent System Design for Stator Windings Faults Diagnosis: Suitable for Maintenance Work

Baccarini¹,

Avelar²,

Silva³

et al. 2013

JSEA

View full text Add to dashboard Cite

The short circuit is a severe fault that occurs in the stator windings. Therefore, it is very important to diagnose this type of failure in its beginning before it causes unscheduled stop and the machine loss. In this context, the Support Vector Machine (SVM) is a tool of considerable importance for standard classification. From some training data, it can diagnose whether or not there is a short circuit beginning, and which is important for predictive maintenance. This work proposes a technique for early detection of a short circuit between the turns aiming at its implementation in a real plant. The paper shows simulation and experimental results, and validates the proposed technique.

show abstract

Incipient fault detection in induction machine stator-winding using a fuzzy-Bayesian change point detection approach

Cited by 51 publications

References 31 publications

Analysis of rotor and stator winding inter-turn faults in WRIM using simulated MEC model and experimental results

Analysis of rotor and stator winding inter-turn faults in WRIM using simulated MEC model and experimental results

A batch-incremental process fault detection and diagnosis using mixtures of probablistic PCA

Intelligent System Design for Stator Windings Faults Diagnosis: Suitable for Maintenance Work

Contact Info

Product

Resources

About