A hybrid training method for ANNs and its application in multi faults diagnosis of rolling bearing

A hybrid training method with probabilistic adaptive strategy for feedforward artificial neural network was proposed and applied to the problem of multi faults diagnosis of rolling bearing. The traditional training method such as LM shows fast convergence speed, but it’s easy to fall into local minimum. The heuristic method such as DE shows good global continuous optimization ability, but its convergence speed is slow. A hybrid training method of LM and DE is presented, and it overcomes the defects by using the advantages of each other. Probabilistic adaptive strategy which could save the time in some situation is adopted. Finally, this method is applied to the problem of rolling bearing faults diagnosis, and compares to other methods. The results show that, high correct classification rate were achieved by LM, and hybrid training methods still continued to converge while traditional method such as LM stopped the convergence. The probabilistic adaptive strategy strengthened the convergence ability of hybrid method in the latter progress, and achieved higher correct rate

Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization

Accurate forecasting of electrical energy consumption of equipment maintenance plays an important role in maintenance decision making and helps greatly in sustainable energy use. The paper presents an approach for forecasting electrical energy consumption of equipment maintenance based on artificial neural network (ANN) and particle swarm optimization (PSO). A multilayer forward ANN is used for modeling relationships between the input variables and the expected electrical energy consumption, and a new adaptive PSO algorithm is proposed for optimizing the parameters of the ANN. Experimental results demonstrate that our approach provides much better accuracies than some other competitive methods on the test data