Leverage big data to predict and anticipate failures within manufacturing.
Industrial processes generate vast volumes of data, but their complete management, including storage and analytics, is not as advanced as expected. The technology exists but is not always applied, sometimes because of ignorance and sometimes because of the belief that the data is unimportant. As a result, manufacturers have been systematically overwhelmed by significant amounts of data, unable to extract the substantial value hidden in the insights they contain.
Advanced analytics help manufacturers solve previously impenetrable problems and reveal those they never knew about, such as hidden bottlenecks, unprofitable production lines or potential failure indicators.
In this article, we will focus on this last point: why it is essential to analyse data in real-time to detect anomalies in the manufacturing process at an early stage.
Identify a malfunction or defect in the manufacturing process
A critical machine fails. Millions of euros are lost in revenue. The worst of all is that it happened on your watch.
The failure was not a human mistake — it was a system failure. But there were missed “signals” along the way, like the temperature probe with “spikes” weeks before. Unfortunately, the site lacks the tools to identify these problems. Data was constantly collected, but identifying a single deviation was like finding a needle in a haystack.
You may have a similar story of equipment failure that has cost your business immense trouble, both in terms of money and effort. There is little or no point in collecting a flood of data in real-time if you don’t manage it correctly. Traditional condition monitoring must be combined with data science and machine learning to find those “signals”.
Vibration
Temperature
Sound
Power
Anomaly detection in manufacturing is identifying data points that lie outside of the ‘norm’ and are rare in occurrence. In the manufacturing industry, anomalies usually are, or strongly indicate, defects in production that will later result in a loss to the manufacturer.
[1] A McKinsey study estimated that process manufacturers’ appropriate use of data-driven techniques “typically reduces machine downtime by 30 to 50 per cent and increases machine life by 20 to 40 per cent”.
It is here, at the intersection of traditional industry, data science, and machine learning, that we unlock incredible value.
1. Anomaly detection models are customisable and scalable:
At ProjectBinder, we tailor the solutions to the unique needs of each manufacturing facility. Additionally, they are scalable, making them suitable for operations of various sizes. Models are trained during the commissioning, running-in, and production phases to grasp the data patterns of specific sensors. They continuously monitor and recognise patterns indicative of potential failures based on historical data.
2. Anomaly detection models easily integrate with existing Systems:
Its smooth integration provides the flexibility to analyse data in the cloud or using local computational power. It allows you to choose the data analysis approach for your operational needs, ensuring optimal efficiency and control over your data processes.
3. How do Anomaly Detection Models Work:
Anomaly Detection harnesses artificial intelligence. Models are trained during the commissioning, running-in, and production phases to grasp the data patterns of specific sensors. They continuously monitor and recognise patterns indicative of potential failures based on historical data.
4. Benefits of Anomaly Detection Models for manufacturers:
- Minimum downtime through proactive identification of potential issues.
- It has improved product quality through early anomaly detection.
- Extended equipment lifespan and maintenance planning.
In conclusion, to avoid failing to use this mountain of potential intelligence, as it is your data, implement the proper methods to manage, store, sort and analyse them to make the best decision for your business.
At ProjectBinder, we can help you put that data to work, gathering information from multiple data sources and taking advantage of machine learning models and visualisation platforms to uncover new ways to optimise processes, from sourcing raw materials to selling finished products.
Contact Kasper Schou Telkamp for an appointment, and he will gladly advise you.
Email: [email protected]
Phone: +45 53765047