Combining the power of humans and machines

ABB engineers are training machine learning algorithms to deliver faster and more reliable remote fleet support and troubleshooting services to passenger ship operators

Combining the power of humans and machines

ABB

ABB is exploring how it can harness the power of artificial intelligence

ABB Marine & Ports delivers support services to passenger shipping customers worldwide via its eight ABB Ability Collaborative Operations Centers. From locations in Europe, Asia and the Americas, ABB experts monitor shipboard systems, coordinate equipment diagnostics and offer 24/7 maintenance services. Real-time data is shared between these centres so staff can help with resolving onboard issues or identifying anomalies before they become faults, regardless of their location. 

Now, ABB is exploring how it can harness the power of artificial intelligence (AI) to improve marine diagnostics and maintenance. The technology optimises condition monitoring, reducing the burden on crew members and engineers, while increasing system reliability, vessel performance and safety.  

The shipping industry is ready to adopt AI on a larger scale and its progress is being driven by several key factors. For instance, machine learning techniques have advanced considerably in recent years, and the software allowing these novel methods to be applied to industrial data sets is now more widely available. Just as significant are the wider availability of historical data and the presence of a digital infrastructure that allows information to be collected from vessels and stored in the cloud at a relatively low cost. 

ABB is playing a key part in this development. Its ‘Electric. Digital. Connected.’ strategy encompasses every element in the digital ecosystem, facilitating the collection of data from connected machines and devices onboard ship, as well as its secure storage in the cloud. 

Using past data to prepare algorithms for a specific purpose is fundamental to modern machine learning methods. In the maritime sector, for example, past maintenance data is combined with operational and failure data to develop a condition-based approach that allows engineers to predict malfunctions ahead of time. 

While historical data is crucial to machine learning, human input is equally important. For the past decade, engineers have used the data to provide services like diagnostics, fault detection and troubleshooting, so they possess insights into customer equipment and systems that cannot be gleaned from data alone. Hence, ABB is developing interfaces to enable experienced engineers to train machine learning algorithms and create AI systems that will provide faster and more reliable services to customers. 

Support engineers often assist customers whose operations have been disrupted by system or component failure. In such cases, the engineer will often manually download and inspect measurements and data from the relevant systems or subsystems to pinpoint the root cause and propose an appropriate solution. As part of this process, they classify the incident or fault. The data set concerning a fault may consist of categorical data in the form of alarm lists, measurements from shipboard sensors and text-based information in the form of service reports or communication between the support team and crew.  

Unlike unsupervised machine learning, in which little or nothing is known about the data, supervised machine learning relies on the data being accurately labelled. The labelled input data is frequently divided into training/validation data sets and test data sets, which are used to prepare and verify the machine learning algorithm.   

In the case of fault diagnostics in complex marine systems, a classification algorithm is employed. The underlying engine used to train the classifier varies depending on the data set, but the training and testing process, as well as the deployment of the trained model into a production system, can all be integrated into the ABB Ability ecosystem. 

Unsupervised algorithms cannot learn structure from labelled data, and instead must identify that structure independently. In cooperation with researchers from Norway’s University of Oslo and as part of the BigInsight research project it is funding and contributing to, ABB has developed an unsupervised system that takes output from marine systems and finds structure or clusters in the unlabelled data set. The algorithm is based on advanced Bayesian statistical methods, accounting for the fact that data sets collected from marine applications do not constitute big data on the scale of other industries, such as the consumer applications market. 

Each of the structures corresponds to a mode of operation of the equipment, including fault or failure modes. However, for these results to be useful, domain knowledge is needed to accurately recognise and name the various clusters or operational modes.  

The developed model allows engineers to manually edit the cluster. For example, for a given fault class, an engineer may decide that a certain alarm or message delivered by the system is irrelevant and should not be considered as an indication by the algorithm. The methodology will be applied to automated diagnostics and fault detection for complex marine systems. It will be integrated into the digital service offering and support on-site engineers in their maintenance work.  

ABB is augmenting its support and troubleshooting services not by replacing human staff with AI, but by developing systems that combine the benefits of data and machine learning with the skills and knowledge that only experienced engineers can offer. The more input the experts provide, the more intelligent the systems will become. This will ultimately improve the level of service provided to the customer, increase vessel efficiency, and make maintenance work easier and safer for the engineer. CFR 

Morten Stakkeland is a data scientist at ABB Marine & Ports 

This article was first published in the Spring/Summer issue of Cruise & Ferry Review. All information was correct at the time of printing, but may since have changed. 

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Rebecca Gibson
By Rebecca Gibson
17 May 2021

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