Spanish oil storage and logistics firm CLH Group has implemented predictive maintenance to assist in the day-to-day management of its logistics network.
The predictive maintenance system is based on data gathering, artificial intelligence and machine learning. Intelligent systems capture information from sensors on more than 700 control valves used to loads road tankers at CLH facilities, and almost 100 pumps on CLH pipelines. This enables equipment to be continuously monitored on online dashboards, while the information and analyses performed, including on the condition of the equipment can be shared throughout the whole company.
Meanwhile models based on artificial intelligence and machine learning monitor the ongoing operation of the assets, taking into account different operating situations, allowing potential malfunctions to be spotted before they happen, and scheduling corrective actions, thus avoiding unplanned downtime and damage to the assets.
‘During the test period, it could be seen that the models developed for control valves had a very high accuracy rate and were capable of identifying malfunctions that result in a loss of efficiency, while making the operation safer,’ says the company. ‘These were developed using agile methodology intended to deliver fast developments and consistent value. The initiative also served to validate the technology before rolling it out to other infrastructure where it can be applied to optimise the entire life cycle of the equipment.’
The project is part of CLH’s digital transformation plan and the company says it aims to ensure fast, safer and more efficient operations.