When explaining what ifm and its moneo solution can provide to a factory or plant, Freddie Coertze said it is like how a smartwatch on your wrist works.
“The moneo DataScience toolbox uses historical data, sensor data, and inputted process parameters to diagnose problems, detect anomalies and predict potential issues. If you think about how a smartwatch might give you an indication of your health. The monitoring of different physical attributes and the information you feed into it supports this,” said ifm Australia’s National IoT Business Manager. “This intelligent toolbox is designed to provide a holistic view of asset health. It also enhances an engineer’s predictive maintenance program and workflow.”
According to Coertze, the moneo DataScience Toolbox enables engineers to take a DIY approach to predictive maintenance with its Smart Limit Watcher tool.
“It is a self-service platform. The algorithmic method of the Smart Limit Watcher can measure and compare data against predicted target values,” he said. “It’s comparable to having an in-house doctor that monitors the health and performance of your assets on an ongoing basis. They provide suggestions for improvement to prevent deterioration and lengthen the asset life.”
Medical history and DataScience toolbox similar
A doctor relies on a patient’s medical history in addition to the information a patient will provide as to what’s good. The moneo DataScience Toolbox needs to learn what is ‘good behaviour’ for an asset.
“It then enables automatic and early anomaly detection. The moneo DataScience toolbox gives users the ability to react quickly to deviations within the production process,” says Coertze. “In other words, you’re not relying solely on historical data to make decisions. You can be proactive about asset management, meaning equipment hasn’t already started to fail by the time you intervene.”
Named after the Latin meaning ‘to warn or advise’, moneo is a solution that can be scaled according to a business’s needs – whether that’s a standalone asset or an entire plant, meaning it is suited to a wide range of applications.
What are the benefits of the moneo DataScience toolbox?
- Self-service, easy to use: No data science expertise required. Provides a simple five-step wizard for users to set up. Data preparation and quality checks are automated.
- Smart: Draws on best-fit artificial intelligence mathematical model to automate learning and verification of monitoring accuracy.
- Reliable: Uses real-time condition-based monitoring and historic data to measure performance against target variables.
- Customised: Alarms and warnings are tailored to individual preferences, including the sensitivity to anomaly detection.
Coertze also points out that implementing the moneo DataScience Toolbox is cost-effective compared to an engineer.
“It’s worth doing the comparison to see the value that moneo can provide to a business. Because it is self-service, you’re excluding the need for data scientist expertise which can cost upwards of $20,000. It’s fast and scalable while providing an excellent price-to-performance ratio. It can be integrated with existing systems,” he concludes.
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