Predictive Maintenance for Cranes
Increase uptime and decrease crane repair costs by applying Artificial Intelligence technology
Predictive maintenance is a proactive asset maintenance strategy that aims to prevent crane breakdown. Artificial Intelligence technology and data analytics are used to predict when equipment is likely to fail before the breakdown actually occurs. Maintenance schedules are then updated based on these forecasts. Incorporating AI on crane maintenance activities results on an increase in uptime and a decrease in repair costs. You can further optimize your maintenance inspection schedule and improve equipment safety, productivity and lifecycle value, saving time and money on emergency repairs.
How predictive maintenance helps increase productivityWhen an equipment breaks, it can have a significant domino effect on the terminal operation. Severe crane breakdowns requiring control system experts to get involved are costly. Consequential costs, resulting from delayed container and vessel shipment schedules, further boost the issue. Predictive maintenance technology is key to prevent this problem.
Your crane data is the source
The crane preventive maintenance is determined by insightful performance data which identify potential weaknesses, challenges and maintenance priorities.
Ports and terminals are generating large amounts of data, coming from different types of sensors, the Crane Management System, the Maintenance System and any other available source. For Predictive Maintenance, these sources are combined to generate algorithms that predict when which failure is likely to occur.
Join the Big Data revolution and accomplish the potential that lies within your data.
Artificial Intelligence and Machine learning to predict a possible standstill of equipmentWatch the presentation below to know all about our pilot project, prediction results, operation benefits, the challenges and expectations.
Discover our Predictive Maintenance application
The AI technology behind our Predictive Maintenance solution can be split up into two main categories: supervised learning and unsupervised learning.
For regularly occurring events, like spreader faults, brake wear and rope stretch, supervised learning techniques are applied, such as LSTM neural networks. The network learns from historical patterns in the data, applies them to the current data, and predicts which failures are likely to occur within a given timeframe.
For rarely occurring events, such as hoist gearbox failures, unsupervised learning techniques are applied, such as autoencoder neural networks. The network learns the patterns of a normal state of the equipment, compares the current data to it and sends out an alarm whenever the data deviates from normal.
Combine engineering knowledge with data science expertiseOur crane engineering knowledge and experience combined with data science expertise add great value to your business. Artificial intelligence harbors great potential for your terminal. Our approach to this challenge is a constantly growing, perfectly coordinated digital portfolio in which innovations are integrated step by step.
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How can you exploit your data?
Contact us for more information about Predictive Maintenance and the possibility to realize the potential that lies within your data. Accumulating data about your assets is of no use if you don’t exploit them. Our experts can tailor a predictive maintenance solution to your specific needs. A Proof of Concept on your own data is also possible, contact us for more information.