Hitachi launches AI-assisted predictive maintenance service for petrochemical plants
In the picture: Detection of abnormalities by the predictive maintenance service for petrochemical plants Hitachi Ltd. has started offering the predictive maintenance service for petrochemical plants. It makes use of artificial intelligence (AI) to automatically classify and analyse the petrochemical plant’s operation status in preparation for the real-time detection of changes in conditions and abnormalities that may be signs of failure. The service collects plant operation data and analyses them by means of the adaptive resonance theory (ART), one of AI-based data clustering technologies, to swiftly detect abnormalities in devices and equipment that constitute the plant arising from multiple causes that are difficult to be discovered by predictive maintenance systems based on ordinary prediction models or by human judgment. It will help reduce not only the workload of operators monitoring the plant’s operation but also the failure occurrence ratio to increase efficiency in operation and maintenance. This predictive maintenance service has been developed and commercialised through a demonstrative trial conducted by Hitachi in collaboration with Showa Denko K.K. at the ethylene plant in Showa Denko’s Oita Complex. It came into service for the practical operation of the plant in October. Oil refineries, petrochemical plants and other plants require the constant maintenance of safe and stable operation. It is common for skilled operators to check the data from the monitoring and control system and carry out on-site patrols to perform visual inspections in order to grasp the operation status of individual devices and equipment for the purpose of monitoring plant operation. However, even when individual devices and equipment in the plant are in normal operation, multiple factors such as the flow rate, pressure and temperature may bring the plant to a halt. The plant operation monitoring is therefore dependent on the judgments of skilled operators based on their experience and […]