Predictive Maintenance
Equipment performance and predictable capacity are critical to manufacturing operations. We help manufacturers reduce downtime and increase productivity by improving asset performance with our turnkey comprehensive predictive maintenance solution.
Our combination of advanced machine monitoring technologies and AI -enabled (Artificial Intelligence) predictive analytics with maintenance leadership enables real-time visualization of streaming data, provides machine health forecasts which in turn increases production efficiency and revenues. We have a successful and accomplished executive team bringing over expertise in manufacturing and industrial software systems which fuses the spirit of hi-technology innovation with rock-solid industrial experience.
Machine Predictive Maintenance Solution Highlights:
- Provide 24/7 dashboard with industry leading analytics for your maintenance team
- Monitor component installed sensors and targeted PLC data
- Connect multiple brands of equipment and sensors
- Prevent unplanned machine failures and conduct maintenance in planned time windows
- Increase capacity and reduce capital expense
Failure or loss of equipment and parts is a common type of industrial loss.In unpredictable situations, such losses often cause production line shutdowns and affect productivity.What's more, it can cause serious breakdown and bring great loss to industrial production. Although current preventive maintenance can eliminate faults in advance to a certain extent, it is also prone to over-maintenance (excessive maintenance frequency) or lack of maintenance (excessive maintenance frequency).
Our predictive maintenance solution combines IIoT, industrial AI, big data, machine learning, and predictive analysis to reduce the uncertainty of equipment maintenance by providing actionable information and accurate predictions, and more reasonable planning of production and maintenance plans.
- Data acquisition: Obtain available data through low-energy wireless sensor networks, and also provide wireless gateways to acquire existing device data.
- Data preprocessing: Filter and sort data, and identify data working condition information.
- Feature extraction: Use as many statistics as possible to describe the overall manufacturing change process and extract data feature values.
- Model training: Use historical data of equipment to train a health model to evaluate health status.
- Equipment failure early warning: Evaluate the fit between the current equipment characteristics and the health baseline, and predict the health status of the equipment; Informs equipment operation and maintenance personnel in time and prevent machine downtime ahead of time.
- Visualization: The identification of equipment health patterns can be monitored in real time through graphs.
Our advanced predictive analytics set us apart as a holistic, technology-driven partner. Add to this our many years of maintenance experience with best-in-class processes and people and you can count on us to deliver actionable insights that will ensure greater performance of your critical assets.
