Automatic detection of crane defects
At areas like European seaports, modern cranes are pushed to increase their moves per hour for ever-higher productivity. Failures resulting from increased use and harsh environmental conditions put crane operators and other personnel at serious risk of injury and fatality. This highlights the critical need for real time non-destructive testing (NDT) techniques and SHM. Scientists developed highly sensitive technology to meet this need with the EU-funded project CRANEINSPECT . The team developed a semi-analytical finite element method model to study ultrasonic guided waves (GWs) for bar-like structures with complex geometries. This helped identify areas in the crane components that are particularly susceptible to corrosion, and then evaluate the use of GWs to inspect these structures. The CRANEINSPECT system employs a hybrid NDT technique exploiting acoustic emission (AE) technology and long range ultrasonics (LRU). A dual-capability sensor array acts as both an AE receiver and an LRU transmitter. The sensor array withstands harsh environmental conditions and adapts to irregular surfaces on the crane. Artificial neural network software automatically detects and sizes defects, classifying them and transmitting the information to a control centre via a wireless transmission protocol. The technology will fill an important market need for NDT and SHM of cranes. This will reduce insurance and maintenance costs and, more importantly, the likelihood of catastrophic failure. Along the way, it will provide important products to strengthen the competitive position of associated businesses such as equipment suppliers and NDT technology manufacturers.
Keywords
Crane, structural health monitoring, seaports, non-destructive testing, acoustic emission, long range ultrasonics, sensor array, defects
