Applications of machine learning to machine fault diagnosis: A review and roadmap

The gear cracks of gear box are one of most common failure forms affecting gear shaft drive. It has become significant for practice and economy to diagnose the situation of gearbox rapidly and accurately. The extracted signal is filtered first to eliminate noise, which is pretreated for the diagnostic classification based on the particle filter of radial basis function. As traditional error back-propagation of wavelet neural network with falling into local minimum easily, slow convergence speed and other shortcomings, the particle swarm optimization algorithm is proposed in this paper. This particle swarm algorithm that optimizes the weight values of wavelet neural network (scale factor) and threshold value (the translation factor) was developed to reduce the iteration times and improve the convergence precision and rapidity so that the various parameters of wavelet neural network can be chosen adaptively. Experimental results demonstrate that the proposed method can accurately and quickly identify the damage situation of the gear crack, which is more robust than traditional back-propagation algorithm. It provides guidances and references for the maintenance of the gear drive system schemes.

Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network

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Particle Swarm Optimization Algorithm with Mutation Operator for Particle Filter Noise Reduction in Mechanical Fault Diagnosis

Nonlinear Lamb wave analysis for microdefect identification in mechanical structural health assessment

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Multi-fault Condition Monitoring of Slurry Pump with Principle Component Analysis and Sequential Hypothesis Test

A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degra- dation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back- propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experi- mental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.

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Ling Tu

Papers

Fault identification of gearbox degradation with optimized wavelet neural network