Our machine learning algorithms
reduce operational costs
and improve management control
of your critical assets
Our products and services
Companies are at different stages in their digital journey. In DIPAI, we don't mind. We assist with sensor instrumentation, data collection, and data transmission to the cloud. This enables the use of data analytics and machine learning. DIPAIs passion is to develop and deploy generic machine learning algorithms to fit your business. Furthermore, we visualize the machine learning results either in our web portal or integrate them into your own cloud solution. Our goal is to help you improve your processes, reach your KPIs, and enhance decisions.
DIPAIs ML algorithms monitor the actual health conditions of your critical assets, providing anomaly detection, diagnostics, and prognostics. This enables just-in-time maintenance for proactive service scheduling.
DIPAI uses ML to provide a comparative analysis of your current consumption and your best use, taking operating conditions into account. Learn from your best and take control of your cost and emissions.
Using AI algorithms, we can find trends and correlations between process parameters not possible to detect with the human eye. This reduces manual labor and tuning, maximizing efficiency, and hence, optimizing your processes.
Our team has strong industrial experience developing computer vision systems for demanding environments and has extensive knowledge of ML to diagnose images and recognize objects, both 2D and 3D.
Keep control through our web portal and receive notifications of early warnings, service measures, and recommendations.
Predictive maintenance detects anomalies, classifies fault types, and estimates the remaining time before failures. This minimizes breakdowns and high costs.
Just-in-time maintenance schedules service procedures when they are actually needed. Optimize your use of spare parts and service personnel today.
Meet emission targets
Lower your emissions and keep track of important KPIs and savings related to your business. Become a leading environmental actor in your industry.
We specialize in vessel engines, auxiliary equipment, hydrogen-driven machinery, electric motors, and batteries.
The maritime industry is especially challenging due to unpredictable operating conditions at the sea. Our Ph.D. colleagues have faced this challenge and gained valuable know-how in the maritime domain and data preprocessing. This is extremely important for reliable ML outputs in our products.
Our ML algorithms are generic. Thus, they are as applicable in land-based industries as in the maritime industry.
Our products can be applied to almost every component which degrades over time, such as, engines, generators, pumps, turbines, compressors, batteries, etc.
Read about our latest media coverage and projects.
More to come!
Soon we will establish our own blog
DIPAI originates from Ph.D. research conducted at the Norwegian University of Science and Technology in Ålesund, which of many is considered as the maritime capital of Norway. Thus, it's no surprise that DIPAI is highly connected to the maritime cluster in Ålesund.
This close connection has helped DIPAI to both understand the needs of the maritime industry and pinpoint improvements towards the UN's sustainability goals. Among them are to optimize maintenance and fuel consumption of critical maritime ship components.
Dr. André Listou Ellefsen
"Received his Ph.D. degree at the Norwegian University of Science and Technology in Ålesund, 2020. His research on predictive maintenance based on deep learning is the foundation of DIPAI.."
Dr. Emil Dale Bjørlykhaug
"Received his Ph.D. degree at the Norwegian University of Science and Technology in Ålesund, 2019. Three years of hands-on-experience in deep learning and software development in Optimar.."
"Received his M.Sc. degree in economics and business development at the National University of Singapore, 2018. Four years of experience as a contract and tender consultant in Bane NOR.."
Dr. Thiago G. Monteiro
"Received his Ph.D. degree at the Norwegian University of Science and Technology in Ålesund, 2021. His research is on physiological sensor fusion for mental fatigue assessment in the maritime domain.."
Birger S. Pedersen
Senior Software Engineer
"Received his B.Sc. degree in automation at the Norwegian University of Science and Technology in Ålesund, 2015. Former system & control engineer in Optimar and software process engineer in Marel.."
A. Maximiliano Crescitelli
"Pursuing his Ph.D. degree at the
Norwegian University of Science and Technology in Ålesund. His research domain is on the use of AI and deep learning for aquaculture applications..."
Meet the board
"VP Project Artec Aqua. Former senior VP Rolls-Royce and Kongsberg Maritime. Extensive experience in business development in the maritime industry.."
Kjell P. Norvoll
"CEO at Nogva Motorfabrikk. Extensive experience with scale-up, growth, and business development in the maritime industry.."
"CFO at Nogva Motorfabrikk. Broad experience in financial management and general management roles through 20 years.."