AHFE Open AccessA Case study on the implementation of maintenance strategies at an energy generation facility
Abstract
This paper examines a case study involving the implementation of preventative maintenance strategies gleaned from a coal power plant's turbo generator. Turbo generators are an essential and fundamental part of the power generation process. Applying the right maintenance strategies and following the right operational procedures are crucial. This is done so that the machinery can be reliable. The research was conducted at a coal-fired power plant with an eye toward the eventual switch to cleaner forms of energy production. This research is essential because it will help inform plans and operational models when renewable energy sources like solar and wind power replace coal-powered turbines. Firstly, this paper describes various approaches to turbo generator module maintenance. Secondly, recommendations for future actions are presented after discussing the effects of human factors on a coal power station.
Keywords: Human factor, maintenance strategies, turbo generator, operational error
DOI: 10.54941/ahfe1003784
Cite this paper
More from this volume
- Explaining algorithmic decisions: design guidelines for explanations in User Interfaces
- Value-driven architecture enabling new interaction models in Society 5.0
- The Removal of Irrelevant Human Factors in a Multi-Review Corpus through Text Filtering
- Accounting trustworthiness requirements in Service Systems Engineering
- Analysis of the behavior of the floating systems used for boundary of river-sea recreational activities area
- A Data retrieval Model for Distributed Heterogeneous Pharmacy Information Sources
- Short-time taxi demand prediction based on Transformer-LSTM in integrated transportation hub
- Hackathon-based software development: Lessons learned from an internal corporate hackathon
- Improving Internet Advertising Using Click – Through Rate Prediction
- Crowdsourcing for Second Language Learning
- Evaluating embedded semantics for accessibility description of web crawl data
- ETL and ML Forecasting Modeling Process Automation System