View source for Forklift Trucks Usage Analysis

{{StudentProjectTemplate
|Summary=This project is about applying machine learning methods to have a better understanding for the usage of forklifts trucks in industrial application.
|Keywords=Data Mining, Sequence modelling, Representation learning, Activity recognition, Time-series data
|TimeFrame=Fall 2019
|References=[1] J.Wang et al., “Deep learning for sensor-based activity recognition: A survey”, in Pattern Recognition Letters, 119, (2019) 3–11 
[2] S. Herath et al., “Going deeper into action recognition: A survey”, in Image and Vision Computing, 60, (2017) 4–21
|Prerequisites=Data Structure and Algorithms, Artificial Intelligence and Learning Systems courses, programming skills for implementing machine learning algorithms
|Supervisor=Kunru Chen, Alexander Galozy
|Level=Master
|Status=Ongoing
}}
Background:

Vehicle manufacturers have certain rules when the equipment is designed, i.e. they expect that their customers use the equipment in their expected manners. In practical life, however, customers might not notice those details: they use the product in the way that they want to use. This fact causes a series of unexpected and unknown behaviors, and the customers might not use the product in a correct way. Without the knowledge about the actual usage of the product, the company would have problems on maintenance service and product improvement.


This project is in collaboration between Halmstad University and an enterprise, which means that the datasets involved are from real industrial application. The objective of this project is the forklift trucks from that company. They would like to know the practical usage of the forklifts with data mining techniques. They have collected signal-based data with high frequency and have applied some basic data pre-processing. There are some background studies derived from the same dataset, which focus on rule-based analysis and lift events definition.


The goal of the project to derive knowledge about forklift trucks activities. Machine learning techniques should be applied for usage analysis. There are also two main challenges in this project: 1) same activities can consist of different patterns in different tasks. For example, the vehicle speed can largely vary when the truck is in a warehouse and when it is working outdoors; 2) activities which happen closely in time can be hard to distinguish from each other, since they have a small or even no period of transition.


Possible directions:
# multi-class classifications and optimization;
# data augmentation for the specific industrial application;
# unsupervised learning to cover and interpret most of the usage.


If you are interested in this project, please contact kunru.chen@hh.se or pay visit to E522.