Methods of condition monitoring and fault diagnosis for induction motors

Thorsen, Olav; Dalva, M.

doi:10.1002/etep.4450090108

Cited by 10 publications

(6 citation statements)

References 11 publications

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“…The collection of data through sensors can be done offline or online, through invasive or non-invasive techniques based on the identification of the fault. A brief overview of various condition monitoring techniques for the identification of fault has been provided in Thorsen and Dalva (1998). Various condition monitoring techniques have been suggested by different authors to attend different types of fault in different operating conditions (Gaeid et al , 2011; Miljković, 2015; Thomson and Orpin, 2002).…”

Section: Literature Reviewmentioning

confidence: 99%

A model for financial viability of implementation of condition based maintenance for induction motors

Gugaliya

Naikan

2019

JQME

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Purpose When induction motors are considered, there is no specific cost model for net savings per year due to condition-based maintenance (CBM) covering various parameters such as downtime, energy, quality, etc. The purpose of this paper is to develop a cost model for the financial viability of the implementation of CBM for induction motors. Design/methodology/approach A literature review has been carried out to identify the existing failure modes of motor, available condition monitoring techniques, the usefulness of CBM and different maintenance models available. Then, a cost model considering all parameters has been proposed. Findings A cost model has been proposed for the maintenance of induction motors. Method for the economic evaluation of the model has also been suggested in the paper. The application of the model has been illustrated through a case study of a steel plant, which suggests that investment in the condition monitoring of induction motors increases the net profit of the organization. Research limitations/implications The proposed model is specifically designed for induction motors. All the motors under consideration are assumed to be of the same specifications, and fault in any motor is supposed to have the same effect on quality, cost, criticality, etc., of the operation and end product. Practical implications This paper will help the maintenance manager in decision making when maintenance action has to be carried out for a given motor under CBM for the better utilization of the equipment and resources. This paper also shows how to compute ROI on CBM investment. Originality/value The paper provides a cost model for the economic evaluation of implementing CBM for induction motors which will be useful to researchers and maintenance managers in effective decision making and maintenance planning. The methodology and the cost models are the original contribution of the authors.

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Section: Literature Reviewmentioning

confidence: 99%

A model for financial viability of implementation of condition based maintenance for induction motors

Gugaliya

Naikan

2019

JQME

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“…It has been extensively proved that different faults produce different frequency components . In other words, faults modify the frequency spectrum of the analyzed signal by changing the energy contents at different frequency regions, according to the fault.…”

Section: Frame Of Referencementioning

confidence: 99%

Real‐time condition monitoring on VSD‐fed induction motors through statistical analysis and synchronous speed observation

Cabal-Yépez

Fernandez‐Jaramillo

García-Pérez

et al. 2014

Int. Trans. Electr. Energ. Syst.

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SUMMARYInduction motors are key elements of every industrial process. A faulty motor produces interruptions on production lines, with consequences in cost, product quality, and safety. The relevance in induction motor monitoring is the ability to detect faults in incipient state. Many proposed methods consider direct connection of motors to the power supply; however, the common practice in industry is to connect them through variable speed drives (VSD), which introduce harmonics into the current supply signal that make the fault identification extremely difficult. This work proposes a statistical analysis through mean, variance, and information entropy computation, combined with sensorless rotating speed estimation for classifying different faults in induction motors using an artificial neural network. The proposed methodology examines the voltage and current signals provided by an industrial VSD that ensures a high certainty on identifying the treated faults at different rotational speed. A field programmable gate array-based implementation is developed to offer an online, system-on-chip solution for real-time condition monitoring.

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“…Many researchers have relied upon the acoustic emission pattern for fault detection and diagnosis of bearings running at low speed . The traditional method for diagnosis of both electrical and mechanical faults has been the current signature analysis . The authors have given the review of different condition monitoring methods for IMs and have demonstrated the applicability of proposed Fuzzy Min‐Max and random forest ensemble model for current signal as the efficiency of the fuzzy rule‐based classifiers has also been found superior in other works .…”

Section: Introductionmentioning

confidence: 99%

“…7,8 The traditional method for diagnosis of both electrical and mechanical faults has been the current signature analysis. [9][10][11] The authors have given the review of different condition monitoring methods for IMs and have demonstrated the applicability of proposed Fuzzy Min-Max and random forest ensemble model for current signal 12 as the efficiency of the fuzzy rule-based classifiers has also been found superior in other works. 13 Researchers have utilized EMD for extracting the intrinsic mode functions IMFs of steady-state current and then applied multiple signal classification algorithm for fault classification into unbalance and broken rotor bar.…”

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confidence: 99%

Detection and diagnosis of induction motor bearing faults using multiwavelet transform and naive Bayes classifier

Saini

Aggarwal

2018

Int Trans Electr Energ Syst

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Summary A novel framework is proposed for the classification of motor bearing faults by using multiwavelet transform and naive Bayes classifier. This work has explored the application of multiwavelet transform to the vibration signatures for extracting the effective fault features. Geronimo‐Hardin‐Massopust multiwavelet filter bank has been employed in this work for multiresolution analysis up to fourth decomposition level. This work has relied upon multiwavelets as the multiwavelets are enriched with the properties which wavelets do not offer simultaneously like compact support and orthogonality without compromising with symmetricity and higher order of vanishing moments. After that, fault features have been extracted from the decomposed vibration signals to utilize the fault information present at different resolution levels. The fault classification has been accomplished with high success rate by using the extracted fault features with the naive Bayes classifier due to its inherent features like robustness to noise, simplicity, and quick convergence even with less training data. The efficacy of the proposed classifier has been verified with both the offline signals and the real‐time signals having different fault severity levels. The outperformance of the proposed classifier has been shown by comparing the classification results with other established supervised classification techniques like probabilistic neural network, Gaussian‐support vector machine, polynomial‐support vector machine, and linear‐support vector machine.

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Methods of condition monitoring and fault diagnosis for induction motors

Abstract: The study gives

Cited by 10 publications

References 11 publications

A model for financial viability of implementation of condition based maintenance for induction motors

A model for financial viability of implementation of condition based maintenance for induction motors

Real‐time condition monitoring on VSD‐fed induction motors through statistical analysis and synchronous speed observation

Detection and diagnosis of induction motor bearing faults using multiwavelet transform and naive Bayes classifier

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