Improving Data quality for LCC prediction Using Cloud Computing
| Reference number | |
| Coordinator | Luleå tekniska universitet - Avdelningen för Drift, underhåll och akustik |
| Funding from Vinnova | SEK 4 250 000 |
| Project duration | April 2018 - June 2021 |
| Status | Completed |
| Venture | The strategic innovation programme for Swedish mining and metal producing industry - SIP Swedish Mining Innovation |
| Call | STRIM fall 2017 |
Important results from the project
The purposes of the project are to: 1.develop a framework for data quality analytics of MAXIMO database, the framework covers three essential aspects: diagnostic, prediction and prescription; 2.develop, validate and demonstrate an economic replacement time (ERT) decision model in the mining environment; 3.build a generic software considering real operational parameters as a prototype demonstration in mining operational environment.
Expected long term effects
The project have the main potential: 1.Have the direct effect of reducing the operating and maintenance costs of mining equipment by optimize its lifetime and minimize the total ownership cost. 2.Asset manufacturers will be able to evaluate the reliability of their machinery and align production with the needs of the market. 3.Have the direct impact on improving data quality of MAXIMO database used in mining industry.
Approach and implementation
Steps of project’s implementation are: 1.Data collection step: historical operating costs, maintenance costs, and acquisition cost data for the case study is collected. 2.Methodology: Mothed for solving data quality problems is developed. 3.A practical optimization model based on the total discounted cost is developed. 4.Results visualization: a GUI is developed to estimate the ERT based on the optimization model. 5.A private cloud computing service is developed in eMaintenance lab at LTU.