Client: UMPI 3D
Location: Abrera, Barcelona, España
Sector: Machinery manufacturing and engineering
Application: IIoT
UMPI 3D uses compressed air for a large part of its processes. Monitoring the air compressor with IIoT and key aspects such as energy consumption and pressure has enabled them to increase equipment availability as well as reduce maintenance and usage costs.
Main objective:
Increase availability and optimize cost
Proposed method:
Grafana and IIoT monitoring
In depth analysis:
Our customer, a company that uses an air compressor in its production process, was facing challenges in terms of equipment availability, power consumption and maintenance costs. The lack of real-time data on temperature, vibration, power consumption and pressure made it difficult to identify problems early and make informed decisions. As a result, equipment availability was 85%, power consumption was high and maintenance costs were significant.
Our Industrial IoT consulting team has implemented an air compressor monitoring system composed of temperature, vibration, power consumption meter and pressure sensors on the air compressor. We used Arduino Machine Control by Arduino PRO to centralize the data acquisition and generate a Modbus TCP to communicate with a gateway that would facilitate the data upload to the cloud. These sensors would collect real-time data and transmit it to the gateway via Modbus TCP. The data would then be stored in the amazon cloud (aws) for further analysis and visualization.
We developed a visual interface using Grafana to enable effective understanding and visualization of data collected in the cloud. This solution has provided greater visibility and insight into compressor performance, enabling data-driven decision making and improvements in operational efficiency.
Air compressor monitoring has lead to:
- Increased equipment availability: from 85% to 95%, generating higher productivity and reducing unplanned downtime.
- Reduced electricity consumption: we achieved a 9% reduction in energy consumption, resulting in significant cost savings.
- Reduced maintenance expenses: reduced by 37%, thanks to real-time monitoring that allowed us to adopt a proactive approach and more effective preventive maintenance.
These highly satisfactory results are evidence of the positive impact of our Industrial IoT solution on your company’s operational efficiency and costs.
In addition, with the successful implementation of this project, the training of a machine learning model for predictive maintenance is being considered. This would allow detecting anomalies and potential problems before they occur, leading to further cost reduction and increased operational efficiency.
Based on the results and demonstrated success, the company is planning to implement IoT in other devices in the facility. This will open up opportunities to improve efficiency, asset management and process optimization across the organization.