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Condition Monitoring of Roller Bearing by K-star Classifier andK-nearest Neighborhood Classifier Using Sound Signal

Structural Durability & Health Monitoring 2017年第1期11卷 1-17页

作者： Rahul Kumar Sharma V.Sugumaran Hemantha Kumar M.Amarnath V.I.T.university ChennaiIndia N.I.T.Karnataka SrinivasanagarSurathkalMangaloreKarnatakaIndia I.I.I.T.D.&M.Jabalpur KhamariaJabalpurIndia Department of Mechanical Engineering School of Mechanical and Building SciencesV.I.T University

Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault ***, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.

关键词： K-star k-nearest neighborhood k-NN machine learning approach conditionmonitoring fault diagnosis roller bearing decision tree algorithm J-48 random treealgorithm decision making two-layer feature selection sound signal statistical features

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Evaluation of thermal imaging system and thermal radiation detector for real-time condition monitoring of high power frequency converters

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Advances in Manufacturing 2014年第1期2卷 88-94页

作者： Anders Eriksen D. Osinski D. R. Hjelme Department of Electrical and Computer Engineering Faculty ofTechnology SOr-TrCndelag University College TrondheimNorway

We have carried out at laboratory test to study the feasibility of using thermal radiation detectors for online thermal monitoring of electrical systems in wind turbines. A 25 kW frequency converter is instrumented with a thermal camera, operating in the 8-14 μm wave- length range, and a single-pixel thermopile sensor, operating in the 4-8 μm wavelength range, to monitor the temperature development of the power electronics under various load sequences. Both systems performed satisfactorily with insignificant temperature deviations when compared to data from calibrated point contact sensor. With spatial averaging over a 7 mm × 7 mm for the camera and temporal averaging over 60 s for the thermo- pile sensor, we reduce the root mean square noise to 45 mK and 68 mK respectively. The low cost and simple operation of the thermopile sensor make it very attractive for condition monitoring applications, whereas the attractive feature of the camera is the possibility of multi-point or distributed temperature measurements.

关键词： Temperature measurement conditionmonitoring Frequency converter Wind turbine

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