Over several years, Fishbone has worked closely with train, automotive and infrastructure engineering and operating companies, to gain a detailed insight into engineering data and the possibilities it presents to asset condition monitoring.
We think of ourselves as ‘engineers informed by data’ because we understand data and the insight it can provide to the engineering domain projects and challenges that we face. Fishbone now has a dedicated team of data scientists and data engineers working on complex problems and specialist algorithms to provide product and system diagnostics, predictive analytics and prognostics for a wide range of solutions including asset condition monitoring and condition-based maintenance.
Having gained a rich understanding of data and digital twin concepts through several engineering domain projects, Fishbone has combined this knowledge to create a suite of products aimed at solving a wide range of engineering, operational and organisational challenges across the transportation sector and the wider industry.
The rapid emergence of the Internet of Things (IoT) has created new possibilities for data acquisition and the provision of high-quality management information. Working closely with academia, Fishbone has grasped these new possibilities and overlaid them onto our existing Engineering, Innovation and Solutions services, to offer products which provide unique insights and improve the performance of assets and organisations.
The FISHTM (Fishbone Information Services Hub) is a cloud-based remote condition monitoring system, powered by advanced Artificial Intelligence (AI), ML and edge computing techniques, providing real-time remote condition, diagnostics and prognostics capabilities to a variety of assets including vehicles, trains, trams, buses and aircraft. Once we have ingested your data into the FISH, we can build bespoke modules to suit customers’ requirements.
Please refer to OTMR Data Analytics for Fault Analysis and Predictive Maintenance case study for further information.
Please refer to FISH case study for further information.
Please refer to Readiness Tool case study for further information.