For Predictive maintenance models, we use historical and real-time data to anticipate equipment failures or maintenance needs. By analysing sensor data, maintenance logs, and other relevant information, we can schedule maintenance proactively, reduce downtime, and extend the lifespan of your machinery.
Data from different production vehicles, tools, and devices can be used to predict when a system needs maintenance, to then schedule preventive maintenance and quality control operations.
This precise prediction of fail of machinery pieces using IoT sensor data reduces equipment downtime, tool failure, and maintenance demands. By monitoring the condition of equipment and infrastructure in real-time (this could be data such as tracking vibrations, recurrent use of machines in a specific order, temperatures and pressures, etc.) we can understand the equipment’s typical operation state and track variations.
By analysing historical maintenance data, we can then generate descriptive models to understand recurrent causes for maintenance to then be able to predict when and where maintenance will be needed in the future.
Our approach is proactive and extends the lifespan of assets ensuring smooth operations, resulting in a reduction of costs, which reduces environmental impact and improves competitiveness.
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- Anomaly detection: we automatically detect early warning signs of a potential equipment breakdown. We focus on identifying all types of anomalies and cover a wide range of data types.
- Empowering decision-making: once we have already detected anomalies, our systems need to be intuitive enough for different stakeholders to understand and visualise. This way, decision makers can provide recommendations on the best course of action based on the identified issues and suggestions on the optimal time for maintenance or advise on the replacement of certain parts.
We mostly use Predictive Maintenance in the following industries:
