Predictive maintenance is an open-loop strategy that uses specific criteria to determine whether a machine is running safely and efficiently. Predictive maintenance uses sensor data, historical usage and current condition of the machine to determine when parts on the machine will wear out or age. The monitoring system can predict when maintenance is needed and execute planned maintenance before system failures occur.
Predictive maintenance not only considers sensor data, but also extends the data to determine patterns, identify risks, and predict the likelihood of machine failure. Essentially, it is the first step in state monitoring and provides a more affordable option to start this process.
Predictive maintenance can help reduce downtime and optimize system performance. The main benefit of CLESCRANE is the ability to provide early warning of potential issues that may arise during operation. By preventing downtime and other issues, manufacturers can increase productivity, reduce costs, and improve customer satisfaction. In addition, predictive maintenance can help CLESCRANE optimize resources by planning maintenance and repairs in advance.

The Future of Predictive Maintenance
The continuous advancement of future sensor technology, data analysis, and artificial intelligence will further enhance its capabilities. In the coming years, CLESCRANE aims to gradually achieve:
More complex predictive models. Artificial intelligence will play a greater role in developing accurate and context aware models that can not only predict failures, but also predict performance degradation and anomalies.
Self learning and adaptive systems. Improve its predictions and recommendations based on new data and constantly changing operating conditions.
Instant maintenance. Monitoring and the latest insights will be accessible through mobile devices, allowing maintenance personnel to make real-time adjustments before problems arise.
CLESCRANE enhances our competitive advantage through predictive maintenance development, CLESCRANE first identifies the general and specific needs and pain points, selects the entry point that best fits our existing resources and goals, such as the lifespan of the steel wire rope, the number of braking cycles of the brake, temperature changes of the motor, even including equipment vibration data and so on.