Sensors & Transducers


Predictive maintenance of motors and gearboxes on large cranes

June 2023 Sensors & Transducers

The global port industry is experiencing significant growth, and more businesses are turning towards automated solutions to predict if motors or gears are beginning to fail. The cranes that move shipping containers are large, and face dangerous and harsh conditions such as extreme temperature, vibration and unpredictable weather. This can cause them to break down or underperform. Most operators and crane employees need to dedicate large amounts of their time to manually check on rotating parts and machines to ensure that they are working properly, cutting into the overall production time.

Recently, an Australian port operator worked with Turck Banner Australia to find a solution that monitors electric motors and gearboxes throughout the entire port facility. These motors and gearboxes are found on the boom and hoist of their Ship-to-Shore (STS) cranes that are used throughout the facility. The company needed a way to monitor vibration and temperature levels so that the components could be serviced before they failed completely, causing excessive downtime and repair costs.

Port monitoring applications typically present many difficult challenges that ordinary sensors cannot solve. The outdoor environment presents unpredictable weather and can cover large areas of land. Ports use large and heavy machines, and they create high levels of friction and vibration, causing equipment to deteriorate quickly.

Downtime, or slow performing machines significantly affect a company’s effectiveness and ability to support the growing global demand. To prevent this from happening, port operators need to routinely perform a manual inspection of the cranes to make sure they are in proper working order. The manual check is not ideal because ports are quite large, and an inspection takes a long time to complete and prevents operators from doing other tasks that would improve the company’s throughput and effectiveness.

Turck Banner’s wireless products was found to be an effective solution for predictive maintenance at these ports. The large outdoor area cannot be wired easily, and wireless products offer the advantage of covering multiple units over a large area. Predictive maintenance allows for the monitoring of changes in machinery performance and allows for corrective action to be taken before a larger issue arises, saving companies time and money on emergency repairs and downtime.

The QM30VT wireless vibration and temperature sensor connects to a battery-powered Q45 wireless node to monitor the motors and gearboxes. The Q45 wireless nodes are paired with a DXM Series wireless controller to collect and interpret the values received from the vibration and temperature sensor. The controller can also be set up to regulate TL70 tower lights that are used to notify the port’s maintenance team. If the vibration or temperature exceeds safe thresholds, then the TL70 will alert the maintenance team to take appropriate action before the problem becomes worse. These set thresholds can be easily adjusted on the DXM controller.

Wireless products and battery-powered devices make installation easy, and the elimination of cables keeps the area free of clutter, especially those that may be hard to reach. Turck Banner offers multiple wireless products that are simple to use and include pre-defined I/O mapping between two devices. The DXM Series controller is also scalable and can communicate with up to 47 sensors or nodes, making it ideal for growing companies.

Turck Banner sensors are manufactured to be rugged and durable, ensuring the products can withstand high vibration and temperature changes, and they work well in demanding environments. The sensors allow for simple installation and operation, resulting in reduced wiring, easy mounting, and an intuitive setup design. All these factors ensure that the end customer receives a solution that can withstand the environmental conditions, and significantly decrease downtime, as a result of predictive maintenance.


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