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This accurate diagnosis result is further proof of the effectiveness of the proposed method in fault diagnosis.
Finally the effectiveness of the proposed method in fault detection of gears is validated using experimental signals from a planetary gearbox test rig.
In this paper we present a novel method in fault recognition and classification in Nuclear Power Plant NPPP) using wavelet transform based Artificial Neural Network (ANN).
Experimental results on three-phase induction motor verify the ability of the proposed method in fault diagnosis despite low quality (noisy) of measured vibration signal.
The performance of the proposed method in fault detection of the rolling element bearings is validated using simulation data and experimental signals from a bearing with multiple faults and two faulty bearings.
Partial PCA based on principal component analysis (PCA) with ideas borrowed from parity relations is a useful method in fault isolation (J. Gertler, W. Li, Y. Huang, T.J. McAvoy, Isolation enhanced principal component analysis, AIChE Journal 45(2) (1999) 323 334).
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The effectiveness of the proposed method in discovering fault characteristics and detecting faults of rolling element bearings is supported using an experimental test.
Stochastic resonance (SR) is widely used as an enhanced signal detection method in machinery fault diagnosis.
Support vector machine (SVM) is a widely used machine learning method in intelligent fault diagnosis.
Simulation and application have confirmed the effectiveness and superiority of the proposed method in gear fault diagnosis.
The advantage of the dynamic clustering method in comprehensive fault sealing property evaluation is to establish a single-factor evaluation matrix.
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