SPOTLIGHT
Using sensory data, cranes embedded with AI will be able to respond in real-time to changes in operating parameters without human input.
business development, Juston Rouse, sees AI as having one key impact on the industry. “It’s cranes that think ahead,” he says. “AI will be embedded from design to daily operation, enabling equipment to self-diagnose, adjust in real time and guide operators toward the safest, most efficient moves.”
The age of the smart crane Rouse and others are in no doubt that AI will become increasingly commonplace in the crane and hoist industry, and its role in material handling will only grow. The result will be cranes that, to some extent, will be able to think for themselves. AI will enable cranes to learn from their environment, their operating parameters, and feedback from every task they handle. Ultimately, smart cranes will be able to
predict key parameters of their operating envelope, such as the weight of a particular load, how a load might behave when moved, what obstacles it might encounter and, therefore, how best to perform a specific lift in the safest and most efficient way possible. Using data from in-built sensors, external cameras and other data-gathering systems, a crane equipped with machine learning capability will constantly improve its performance as the dataset on which it bases its decision-making processes becomes larger and richer. Ultimately, it will be able to not only
perform specific tasks automatically, but it will be able to respond in real-time to changes in operating parameters – such as a shift in its load – without requiring human input. The vision is of crane systems that can perform lifts with incredible precision, requiring human supervision but not intervention, leading to greater efficiency and less downtime due to its optimised performance criteria. Smarter, faster decisions lead to greater efficiency, and the speed of data analysis and decision-making that an AI can achieve far outstrips anything a human brain can achieve. Furthermore, cranes with embedded AI can not only operate faster, but can also stick to repetitive tasks without tiring, getting sick, or needing a holiday. This could have a hugely beneficial impact on both cost and safety. Load stabilisation is a key component of operational efficiency, and AI can learn to effectively manage load sway in a way that is comparable to a skilled human operator, without the risk of human error. The result is faster and more precise material handling and optimised movement of loads as the AI learns and combines a wealth of historical data with real-time information on current conditions to optimise lifting patterns, improve material flow and significantly reduce operation times. In terms of cost, the benefit accrues from both the efficiency gains in performing lifts, and
the ability of AI to predict when wear and tear are taking their toll on mechanical elements. Systems that are constantly monitoring performance criteria are highly sensitive to any change in how the crane is moving and handling loads. This data is the lifeblood of predictive maintenance, which cuts down expensive breakdowns and minimises any potential downtime, as a minor repair is always quicker and cheaper to perform than a major overhaul. Predictive maintenance stems from condition-based monitoring, with embedded sensors constantly feeding data to the AI system about the condition of core crane components. From that data, machine learning algorithms consistently improve their failure prediction capability, forecasting with ever- greater accuracy when components are likely to fail, and allowing for scheduled maintenance before any unforeseen breakdowns can occur. The result is greatly extended equipment life and, consequently, reduced capex on repairs and replacement. When it comes to safety, the impact could be just as significant. AI-enabled equipment is able to continuously monitor its surroundings, thereby becoming more adept at avoiding collisions, which greatly improves safety for both workers and other equipment in the vicinity. Furthermore, the reduced need for
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