Condition-Based Failure-Free Time Estimation of a Pump.
Grzegorz ĆwikłaIwona PaprockaPublished in: Sensors (Basel, Switzerland) (2023)
Reliable and continuous operation of the equipment is expected in the wastewater treatment plant, as any perturbations can lead to environmental pollution and the need to pay penalties. Optimization and minimization of operating costs of the pump station cannot, therefore, lead to a reduction in reliability but rather should be based on preventive works, the necessity of which should be foreseen. The purpose of this paper is to develop an accurate model to predict a pump's mean time to failure, allowing for rational planning of maintenance. The pumps operate under the supervision of the automatic control system and SCADA, which is the source of historical data on pump operation parameters. This enables the research and development of various methods and algorithms for optimizing service activities. In this case, a multiple linear regression model is developed to describe the impact of historical data on pump operation for pump maintenance. In the literature, the least squares method is used to estimate unknown regression coefficients for this data. The original value of the paper is the application of the genetic algorithm to estimate coefficient values of the multiple linear regression model of failure-free time of the pump. Necessary analysis and simulations are performed on the data collected for submersible pumps in a sewage pumping station. As a result, an improvement in the adequacy of the presented model was identified.
Keyphrases
- wastewater treatment
- electronic health record
- machine learning
- big data
- deep learning
- systematic review
- healthcare
- mental health
- antibiotic resistance genes
- magnetic resonance imaging
- artificial intelligence
- gene expression
- mass spectrometry
- particulate matter
- human health
- dna methylation
- microbial community
- molecular dynamics
- neural network
- climate change
- anaerobic digestion