Fault diagnosis for rotary machinery with selective ensemble neural networks

•Selective ensemble learning is developed for fault diagnosis of rotary machinery.•Adaptive optimizing method is employed to select better base classifiers.•Generalization performance is improved in fault diagnosis issues.•Validity is verified by experiments under various conditions and ambient nois...

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Veröffentlicht in:	Mechanical systems and signal processing 2018-12, Vol.113, p.112-130
Hauptverfasser:	Wang, Zhen-Ya, Lu, Chen, Zhou, Bo
Format:	Artikel
Sprache:	eng
Schlagworte:	Classifiers Component reliability Ensemble classification Ensemble learning Fault diagnosis Feature extraction Iterative methods Linear transformations Machinery and equipment Matrix algebra Matrix methods Neural networks Optimization Particle swarm optimization Performance enhancement Rotary machinery Selective ensemble learning Singular value decomposition Statistical analysis Vibration Weight
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