Implementation of Predictive Models in Industrial Machines with Proposed Automatic Adaptation Algorithm

Abstract

This article describes in more detail the issue of using predictive models of NAR neural networks to predict the course of certain quantities, which may indicate a problem with the industrial machines or their major failures. It is very important to find sufficient size of the structure and values of parameters that directly affect the output accuracy of the model. This article presents the way in which it is possible to automatically find the settings of these NAR models so that the required final accuracy metric is achieved. This presented algorithm was tested on simulation data samples collected by using the M5StickC microcontroller device. This collected dataset presented in this article contains accelerometer and gyroscopic data only, but there is a possibility to expand and add some other sensors to this microcontroller, to collect some other relevant data. This M5-StickC microcontroller device can be used for gathering data in the first phase of the machine state analysis without interfering with the mechanical construction and electrical connections of the machine. Testing of proposed algorithm was carried out in MATLAB environment. The article also describes the way in which these predictive NAR neural network models can be implemented directly in control systems, specifically PLCs from the manufacturer SIEMENS without the use of 3rd party analytical platforms. This application can be helpful in the area of predictive maintenance tasks, especially in avoiding critical failures of industrial machines and devices, or some of their specific parts.

Keywords

• control systems
• predictive maintenance
• NAR neural networks
• analytical module