Fault diagnosis for rotary machinery with selective ensemble neural networks

• Published in: Mechanical systems and signal processing 2018-12, Vol.113, p.112-130
• Main Authors: Wang, Zhen-Ya, Lu, Chen, Zhou, Bo
• Format: Article
• Language: English
• Subjects: 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
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2017.03.051

•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 noise. The diagnosis of rotary machinery systems is gaining interest both in academic and industry fields, which assures machinery operational safety and reliability in terms of typical rotary machinery components such as bearings and gears. With a view to gain better generalization ability of fault diagnosis along with multiple monitored variables with corresponding fault patterns, a novel fault diagnosis method (particle swarm optimization based selective ensemble learning, PSOSEN) that utilizes ensemble learning with differentiated probabilistic neural networks (PNNs) is proposed, where nonlinear decreasing inertia weight based adaptive particle swarm optimization (APSO) is employed to effectively reinforce the learning process by selecting superior individuals for integration instead of all. First, statistical features in the time domain and frequency domain are extracted and integrated from vibration signals, and feature selection based on bagging feature representation is applied to generate desirable PNNs. Second, APSO is used to improve the performance by balancing diversity and accuracy, aiming to eliminate similar individuals via weight assignation and retain the classifiers with better performance in the initial iteration. The globe-best vectors are then, by means of linear transformation, mapped into a matching matrix in which row vectors indicate the corresponding weights of the selected classifiers. Singular value decomposition (SVD) is employed on the established matrix, where an optimal weight vector is thus obtained according to the orthogonal matrices parameters. The fault diagnosis result is finally achieved by ensemble computing of PNNs based on the calculated weight coefficients. Comparative experiments are included in this paper to demonstrate the effectiveness in fault diagnosis of rotary machinery including varying working conditions and different severe degrees.