Soft Sensor Approach for Modeling Ball Mill Load Parameters Based on a Multi-task RNN-LSTM

摘要

In recent years, rapid developments in technology have improved the collection of massive data from different industrial processes. These new developments have also brought benefits for many machines such as ball mills. It is now possible to develop soft sensors for the measurement of load parameters. Vibration and acoustical signals produced by ball mill shells, during the grinding process, contain useful information which is used to determine load parameters inside the equipment. The shell vibration signals and acoustical signals are called secondary variables. They are easy to acquire. The desired variables, the load parameters, are called the primary variables. For the aforementioned, in the present work, vibration and acoustical signals from the ball mill shells are used for modeling a soft sensor. The literature mentions three parameters for the load in ball mills:Material Ball Volume Ratio (MBVR), Pulp Density (PD) and Charge Volume Ratio (CVR). The soft sensor should be able to measure these three variables. Most of the approaches use three models of soft sensors, thus one for each variable (MBVR, PD and CVR). Nonetheless, this thesis proposes a multi-task model capable of calculating the three different variables at the same time as well as making the approach efficient and simple to model. When modeling of a soft sensor, first, it is necessary to sample the signals. Secondly, the data must be pre-processed, and then performs the extraction of characteristics by a neural network (NN). Finally, to find the intrinsic relationship between the secondary variables and the primary variable, a multi-task Recurrent Neural Network (RNN) based on Long Short Term Memory (LSTM) is modeled.
　　This thesis focuses on the implementation of a multi-task soft sensor to measure the load of ball mills, highlighting the multi-task feature, calculating all load parameters (MBVR, PD and CVR) in a single iteration, solving the tedious problem of modeling three systems, making a simple model, and maintaining the precision. The procedure is conceivable since the parameters share a similar statistical distribution. In other words, the parameters share a common structure because all the load parameters belong to the same parameters in a universe, which is the Mill Load (ML) inside the ball mill. The model is based on a RNN-LSTM. Due to the ability of RNN-LSTM to work with temporal sequence, like in the load parameters for ball mills, this kind of network is chosen for modeling a soft sensor.
　　RNN-LSTM work are dependent on time, making recursive the input

目录

声明

Chapter 1 Introduction

1.1 Background and Research Objectives

1.2 State of the Soft Sensors Researches

1.3 Characteristics Extraction and Modeling Techniques

1.4 Main Research Content

Chapter 2 Main Concepts of Soft Sensors for Ball Mills

2.1 The Ball Mill Working Principles

2.2 Theory and Measurement Methods of Soft Sensors

2.3 Chapter Summary

Chapter 3 Deep Learning Concepts

3.1 DNNs

3.2 Deep Learning and RNN-LSTM

3.3 Chapter Summary

Chapter 4 Modeling of a Soft Sensor Based on RNN-LSTM

4.1 Acquisition Data for Multi-Task Model and Pre-Processing

4.2 Soft Sensor Modeling

4.3 Results

4.4 Chapter Summary

Chapter 5 Summary and Perspectives

5.1 Work Summary

5.2 Perspectives

Appendix

参考文献

Thanks

List of academic papers published during the degree