Low Complexity Motor Current Signature Analysis Using Sub-Nyquist Strategy With Reduced Data Length

Naha, Arunava; Samanta, Arunkumar; Routray, Aurobinda; Deb, Alok Kanti

doi:10.1109/tim.2017.2737879

Cited by 38 publications

(22 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vice versa the embedded systems used for on-line diagnosis are designed to perform with a relatively small number of data. Authors in [20] present a method of optimizing sample data size and complexity of calculations. The sub-Nyquist algorithm has been used for the achievement of the above goal.…”

Section: Review Of Methodsmentioning

confidence: 99%

Generalized Roughness Bearing Faults Diagnosis Based on Induction Motor Stator Current

Andrijauskas¹,

Vaitkunas²,

Adaškevičius³

2018

RADIOENGINEERING

View full text Add to dashboard Cite

Despite their reliability, induction motors tend to fail. Around 41% of faults in motors are bearing related and that is the most common fault in motor field. Due to the lack of research on generalized roughness bearing fault diagnostics by use of a stator current spectrum, the presented study analyses both single-point and generalized roughness bearing faults and their classification possibilities. In this paper, a new method for generalized roughness ball bearing fault identification by use of a stator current signal analysis is presented. The algorithm relies on Discrete Wavelet Transform and Welch's spectral density analysis. The composition of both methods is used for building a feature vector for the classifier. In order to achieve classification, support vector machine classifier with linear kernel function has been applied. The validation experiment and results are presented.

show abstract

Section: Review Of Methodsmentioning

confidence: 99%

Generalized Roughness Bearing Faults Diagnosis Based on Induction Motor Stator Current

Andrijauskas¹,

Vaitkunas²,

Adaškevičius³

2018

RADIOENGINEERING

View full text Add to dashboard Cite

show abstract

“…Many researchers are committed to developing early fault diagnosis (EFD) techniques to monitor the health conditions of rotating machinery [17][18][19][20][21]. At present, many EFD techniques have been successfully applied in the modern industries, such as the vibration-based EFD method [22], current-based EFD method [23], acoustic emission-based EFD method [24], sound-based EFD method [25], torque-based EFD method [26], and rotating encoder-based EFD method [27], etc. Among these sensor signals, vibration-based diagnostic method is the most commonly researched one because vibration signals can directly represent the dynamic behavior of rotating machinery [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

155

View full text Add to dashboard Cite

Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

show abstract

“…Indeed, motor 24 current signature analysis (MCSA) has become a notable approach for IM fault diagnosis, because the currents of a faulty IM contain frequency components which can be related to both mechanical faults (rolling bearings, eccentricity) and electric and magnetic asymmetries (stator winding short-circuits, rotor broken bars) [10]. Besides, MCSA is non-invasive, and it has low hardware (just a clip-on Hall effect probe) and software requirements [11][12][13] (Fourier Analysis). Fig.…”

Section: Introductionmentioning

confidence: 99%

Induction machine model with finite element accuracy for condition monitoring running in real time using hardware in the loop system

Sapena-Bañó

Chinesta

Pineda-Sánchez

et al. 2019

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

Most industrial processes are run by induction machines (IMs). Condition monitoring of IM assures their continuity of service, and it may avoid highly costly breakdowns. Among the methods for condition monitoring, online motor current signature analysis is being attracting a rising interest, because it is non-invasive, and it can identify a wide variety of faults at early stage. To favour the development of on-line fault diagnosis techniques, it is necessary to have real-time currents with which test the new techniques and devices. Models running in real time in hardware-in-the-loop (HIL) simulators are a suitable alternative to balance the drawbacks of test benches (costly, limited machines, faults and working conditions). These models must be accurate enough to reflect the effects of a fault and they must be running in real time. A promising technique based on the equivalent circuit parameters calculation of IM by finite element analysis (FEA) is attracting a rising interest due to its reliability, performance and the possibility of being run in a HIL. Nevertheless, prior to running in a HIL, it is necessary to compute the IM parameters using FEA, which requires long simulation times and high computing resources. Consequently, covering a whole range of degrees of a giving fault could be unaffordable. What is proposed in this paper is to apply the sparse subspace learning (SSL) in combination with the hierarchical Lagrangian interpolation (HLI) to obtain the parametric solutions of the faulty IM model that cover the whole range of severity of a given fault, with a reduced number of FEA simulations. By means of this approach it is possible not only to boost the computation speed but also to achieve a significant reduction of memory requirements while retaining reasonable accuracy compared to traditional FEA, so enabling the real-time simulation of predictive models.

show abstract

Low Complexity Motor Current Signature Analysis Using Sub-Nyquist Strategy With Reduced Data Length

Cited by 38 publications

References 62 publications

Generalized Roughness Bearing Faults Diagnosis Based on Induction Motor Stator Current

Generalized Roughness Bearing Faults Diagnosis Based on Induction Motor Stator Current

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Induction machine model with finite element accuracy for condition monitoring running in real time using hardware in the loop system

Contact Info

Product

Resources

About