A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals and oil refineries

Thorsen, Olav; Dalva, M.

doi:10.1109/pcicon.1994.347637

Cited by 134 publications

(38 citation statements)

References 5 publications

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“…In practice, the standard life for each of such machines is over 25, 000 operating hours, but they are interrupted or fail due to application of wrong rated power, imperfection in fabrication or construction, misuse of the machine, mistakes and additional damage during repairs. The chart in figure 3 presents some major faults that can be found in electrical machine based on the survey carried out by Thorsen & Dalva on distribution of failed components on induction machines [11]. …”

Section: Taxonomies Of Electrical Machine Faultsmentioning

confidence: 99%

Origin and manifestation of electrical machine faults-A review

Imoru

Jimoh

Hamam

2014

The 2nd IEEE Conference on Power Engineering and Renewable Energy (ICPERE) 2014

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This paper discusses the review of origin of various faults in electrical machines and how each fault manifests itself. It appears that no single technique is able to easily handle all types of faults from previous research works, because different diagnostic technique is used to diagnose different types of faults. This paper also describes the possibility of unifying diagnosis techniques in such a way that faults are easily traced or detected on-line. Often faults are analysed only after the occurrence and the origin of the fault could be far remote from the manifestation. The description in the paper begins to work towards a unified fault diagnostic system suitable for on-line early detection long before their manifestation. Towards this end the origin and manifestation of various faults in electrical machines are critically examined in the review.

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Section: Taxonomies Of Electrical Machine Faultsmentioning

confidence: 99%

Origin and manifestation of electrical machine faults-A review

Imoru

Jimoh

Hamam

2014

The 2nd IEEE Conference on Power Engineering and Renewable Energy (ICPERE) 2014

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“…It is known that stator faults account for 30-40 % of motor failures and between half and two-thirds of these are stator failures due to winding short-circuits [21]. A common way to detect stator fault is based on motor current signature analysis (MCSA).…”

Section: Application To Fault Diagnosis For Stator Winding Short-cmentioning

confidence: 99%

A Hybrid Model with a Weighted Voting Scheme for Feature Selection in Machinery Condition Monitoring

Zhang

Ball

et al. 2007

2007 IEEE International Conference on Automation Science and Engineering

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Feature selection method has become the focus of research in the area of engineering data processing where there exists a large amount of high-dimensional data from the high-frequency acquisition system. For high-dimensional data processing, engineers often resort to feature extraction methods and statistical theories to convert the original features into new features. However, the converted data always lose the engineering meaning of the original features and the choice and use of conversion methods are challenging. In this paper, a hybrid feature selection model is presented to select the most significant input features from all potentially relevant features. The algorithm combines a filter model with a wrapper model. In the filter model, four variable ranking methods are used to pre-rank the candidate features. These four methods including Pearson correlation coefficient, Relief algorithm, Fisher score and Class separability, measure features from various angles, which leads to different ranking results. Therefore, a weighted voting scheme is introduced to re-rank features based on the degree of significance of the four methods on the classification error rate of Radial Basis Function (RBF) classifier. In wrapper model, a Binary Search (BS) method and a Sequential Backward Search (SBS) method are utilized to minimize the number of relevant features when promising to keep the classification error rate of RBF classifier below a given threshold. To demonstrate the potential of applying the method to large-scale engineering data processing, a case study is conducted.

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“…In addition, statistical information on machine failures is available, e.g. [11], but this does not provide information on the failure behavior. This paper will experimentally investigate the failure mode(s) of round enameled wires.…”

Section: Introductionmentioning

confidence: 99%

Experimental determination of stator winding failure behavior

Geest

Polinder

Ferreira

2014

2014 16th European Conference on Power Electronics and Applications

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In the design of fault-tolerant or fail-safe systems, detailed knowledge of potential failure processes is required. One failure mode in electrical machines is an inter-turn, or turn-to-turn short-circuit, which can cause very large local heating and subsequently, progression of the fault. This paper experimentally determines the failure process and mode of enameled wire coils after occurrence of an initial fault.Step currents are applied to dummy coils and the failure process is monitored. Various current levels are applied to single and three layer coils. The results demonstrate a high repeatability between identical tests; a calibrated adiabatic model can predict the time-to-failure. Stepwise changes in the terminal resistance indicate that failure of individual turns occurs and can be distinguished. Lastly, the results prove that that multilayer coils can sustain larger total currents due to formation between the layers.

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A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals and oil refineries

Cited by 134 publications

References 5 publications

Origin and manifestation of electrical machine faults-A review

Origin and manifestation of electrical machine faults-A review

A Hybrid Model with a Weighted Voting Scheme for Feature Selection in Machinery Condition Monitoring

Experimental determination of stator winding failure behavior

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