Electrical Monitoring under Transient Conditions: A New Paradigm in Electric Motors Predictive Maintenance

Antonino-Daviu, José A.

doi:10.3390/app10176137

Cited by 31 publications

(19 citation statements)

References 37 publications

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“…Steady-state analysis employs FEM to model the machine in greater detail. Focus has shifted into flux analysis, due to sensory advancements and its richer harmonic content [4]. Cutting edge approaches investigate AI and Fuzzy Cognitive Maps (FCM) decision making to distinguish fault indications.…”

Section: Literature Reviewmentioning

confidence: 99%

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Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects

Falekas

Karlis

2021

Energies

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State-of-the-art Predictive Maintenance (PM) of Electrical Machines (EMs) focuses on employing Artificial Intelligence (AI) methods with well-established measurement and processing techniques while exploring new combinations, to further establish itself a profitable venture in industry. The latest trend in industrial manufacturing and monitoring is the Digital Twin (DT) which is just now being defined and explored, showing promising results in facilitating the realization of the Industry 4.0 concept. While PM efforts closely resemble suggested DT methodologies and would greatly benefit from improved data handling and availability, a lack of combination regarding the two concepts is detected in literature. In addition, the next-generation-Digital-Twin (nexDT) definition is yet ambiguous. Existing DT reviews discuss broader definitions and include citations often irrelevant to PM. This work aims to redefine the nexDT concept by reviewing latest descriptions in broader literature while establishing a specialized denotation for EM manufacturing, PM, and control, encapsulating most of the relevant work in the process, and providing a new definition specifically catered to PM, serving as a foundation for future endeavors. A brief review of both DT research and PM state-of-the-art spanning the last five years is presented, followed by the conjunction of core concepts into a definitive description. Finally, surmised benefits and future work prospects are reported, especially focused on enabling PM state-of-the-art in AI techniques.

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Section: Literature Reviewmentioning

confidence: 99%

“…Great reviews, such as [1][2][3][4][5][6], concerning state-of-the-art PM methods and their application have been published in recent years, addressing techniques with their applications and comparison. Literature highlights a need for evaluation benchmarks and new combinations.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects

Falekas

Karlis

2021

Energies

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“…In addition, some fault diagnosis techniques benefit from signals of the machine obtained naturally during start‐up and switch‐off transients. It has been demonstrated that fault signatures are more detectable in these cases, and their dynamic behaviour reveals useful information for distinguishing different types of faults and also, phenomena causing false alarms such as rotor core magnetic anisotropy [3, 79].…”

Section: Magnetic Flux‐based Fault Signaturesmentioning

confidence: 99%

“…Various types of quantities, including mechanical vibration, temperature, noise, electrical current, and voltage, have been studied for this purpose. Nonetheless, after many years now, the consensus is that there is no method based on a single quantity that can provide a comprehensive knowledge of the machine's health conditions [3]. Most commercialised techniques are based on motor current signature analysis (MCSA), which has been proved to be susceptible to false/missed alarms.…”

Section: Introductionmentioning

confidence: 99%

Airgap and stray magnetic flux monitoring techniques for fault diagnosis of electrical machines: An overview

Mazaheri‐Tehrani

Faiz

2021

IET Electric Power Appl

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Flux-based fault diagnosis methods have attracted ever-increasing attention from technical communities because of their potential diagnostic capabilities and ability to overcome weaknesses of the traditional methods such as motor current signature analysis (MCSA). This study reviews recent literature on these methods and addresses their strengths and limitations. Different types of flux sensors that can be used for fault diagnosis applications are also discussed. Flux-based approaches are classified based on the type of utilised flux into internal (airgap flux) and external (stray flux) methods and compared with each other. The most common faults in both induction machines (IMs) and permanent magnet machines (PMMs), including inter-turn short-circuit (ITSC), airgap eccentricity (AGE), rotor broken bars (BRB), and irreversible demagnetisation (IDM) faults, are considered in this review. Finally, research gaps are identified, and some suggestions for future research in this area are provided.

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“…Space vector angular fluctuation is a method where the difference between the space vector angle and the angle of an ideal space vector is considered in the frequency domain [11]. A transient analysis that has become widely used in recent years is ATCSA, which is based on time-frequency analysis of the motor current [12,13]. ATCSA has also been applied on VSD-driven motors for the diagnosis of different kinds of faults [4].…”

Section: Introductionmentioning

confidence: 99%

Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps

et al. 2021

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The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainly used for motor diagnosis. As a test item, a VSD-driven circulation pump was measured in a pump test bench. Compared to MCSA, both LoPoFIA and ATCSA showed improvements in terms of minimizing false alarms. However, LoPoFIA simplifies the separation of bearing defects and impeller defects, as impeller defects especially influence higher flow ranges. Compared to LoPoFIA, ATCSA represents a more efficient method in terms of minimizing measurement effort. In summary, both LoPoFIA and ATCSA provide important insights into the behavior of faulty pumps and can be advantageous compared to MCSA in terms of false alarms and fault separation.

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Electrical Monitoring under Transient Conditions: A New Paradigm in Electric Motors Predictive Maintenance

Cited by 31 publications

References 37 publications

Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects

Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects

Airgap and stray magnetic flux monitoring techniques for fault diagnosis of electrical machines: An overview

Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps

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