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The purpose of this research paper is to develop and analyze micro-electro-mechanical systems sensor for vibration monitoring of pumping aggregate.

The system is based on smart sensor and smart mobile phone.

The numerous measurements on a wide range of turbo aggregates were performed to establish the operating condition of pumping aggregates.

Afterwards, the influence of vibration at different positions on the output vibration of the pumping aggregate was analyzed by adaptive neuro fuzzy inference system method.

The purpose of this study is to establish a vibration prediction of pellet mills power transmission by artificial neural network. Vibration monitoring is an important task for any system to ensure safe operations. Improvement of control strategies is crucial for the vibration monitoring.

As predictive control is one of the options for the vibration monitoring in this paper, the predictive model for vibration monitoring was created.

Although the achieved prediction results were acceptable, there is need for more work to apply and test these results in real environment.

Artificial neural network (ANN) was implemented as the predictive model while extreme learning machine (ELM) and back propagation (BP) learning schemes were used as training algorithms for the ANN. BP learning algorithm minimizes the error function by using the gradient descent method. ELM training algorithm is based on selecting of the input weights randomly of the ANN network and the output weight of the network are determined analytically.