SVM practical industrial application for mechanical faults diagnostic

Baccarini, Lane Maria Rabelo; Silva, Valceres Vieira Rocha e; Menezes, B.R.; Caminhas, Walmir M.

doi:10.1016/j.eswa.2010.12.017

Cited by 112 publications

(53 citation statements)

References 16 publications

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“…Recently, SVM were used extensively in many researches for machinery fault diagnosis such as in rolling element bearing (REB) [6], reciprocating compressor valve [7], induction motors [8] and gears [9]. For non-linear case of SVM the equation can be expressed as:…”

Section: Svm In D-s Applicationmentioning

confidence: 99%

Integration of Artificial Intelligence into Dempster Shafer Theory: A Review on Decision Making in Condition Monitoring

Rosli

Hee

Leong

2015

AMM

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Abstract. Machines are the heart of most industries. By ensuring the health of machines, one could easily increase the company revenue and eliminates any safety threat related to machinery catastrophic failures. In condition monitoring (CM), questions often arise during decision making time whether the machine is still safe to run or not? Traditional CM approach depends heavily on human interpretation of results whereby decision is made solely based on the individual experience and knowledge about the machines. The advent of artificial intelligence (AI) and automated ways for decision making in CM provides a more objective and unbiased approach for CM industry and has become a topic of interest in the recent years. This paper reviews the techniques used for automated decision making in CM with emphasis given on Dempster-Shafer (D-S) evident theory and other basic probability assignment (BPA) techniques such as support vector machine (SVM) and etc.

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Section: Svm In D-s Applicationmentioning

confidence: 99%

Integration of Artificial Intelligence into Dempster Shafer Theory: A Review on Decision Making in Condition Monitoring

Rosli

Hee

Leong

2015

AMM

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“…A relatively new AI method, SVM is also used as an artificial intelligence technique for the fault detection of IM [5,8]. In order to compare these AI methods for fault diagnosis, Samanta [9] performed the gear fault detection using ANN and SVM with genetic algorithm, and concluded that the SVM can perform well in comparison with ANN even with smaller number of samples and also training time is less in case of the SVM.…”

Section: Introductionmentioning

confidence: 99%

“…Improved ant colony optimization (IACO) algorithm was used for optimization of the SVM parameter. Baccarini et al [8] presented a practical industrial application of the SVM for mechanical faults diagnostic of IMs based on frequency-domain signals. However, bearing faults which is one of critical faults in IM were not included.…”

Section: Introductionmentioning

confidence: 99%

Taxonomy of Induction-Motor Mechanical-Fault Based on Time-Domain Vibration Signals by Multiclass SVM Classifiers

Gangsar

Tiwari

2016

Intell Ind Syst

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In the present work, faults in induction motors (IM) have been diagnosed by multiclass support vector machine (SVM) algorithms based on time domain vibration signals. The main focus is to classify mechanical faults of induction motors, i.e. the bearing fault, unbalanced rotor, bowed rotor and rotor misalignment at different rotational speeds and diverse loading conditions. In this work, an induction motor test setup was used to generate vibration signals of seeded mechanical faults. For the effective fault diagnosis, one-versus-one multiclass SVM approach with the Gaussianradial basis function (RBF) kernel has been used. For the fault classification, firstly optimum statistical features from higher statistical moments have been selected. Also the selection of SVM kernel parameters, numbers of feature datasets and optimum ratio of training-to-testing data have been performed. The SVM classifier is trained and tested at the same rotational speeds as the measured data as well as innovatively tested at intermediate rotational speeds for which measured data was not available. It is observed that classification accuracy gradually increases with the increase of the rotational speed and with the increase of the load on the IM.

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“…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%

“…The methods based on electrical current analysis are not invasive and they do not require system interruption [2,3,7,19]. Measurement is made by common sensors (Current Transformers) and often, they are already present in the application being monitored.…”

Section: Introductionmentioning

confidence: 99%

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

Baccarini¹,

Avelar²,

Silva³

et al. 2013

JSEA

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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.

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SVM practical industrial application for mechanical faults diagnostic

Cited by 112 publications

References 16 publications

Integration of Artificial Intelligence into Dempster Shafer Theory: A Review on Decision Making in Condition Monitoring

Integration of Artificial Intelligence into Dempster Shafer Theory: A Review on Decision Making in Condition Monitoring

Taxonomy of Induction-Motor Mechanical-Fault Based on Time-Domain Vibration Signals by Multiclass SVM Classifiers

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

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