Predictive Maintenance and Analytics Models
Get a better sense of what is coming with predictive maintenance
Foresee defects and downtime before they happen with predictive maintenance
In-line Defect Prediction with Predictive Maintenance and Analytics
No manufacturing line is finished without testing. Unfortunately, quality feedback is generally not received until final testing at the very end of the line. That’s where predictive maintenance and analytics come in.
What if you could predict defects in the products, recognizing issues within your processing window before they affect yield? Better yet, what if you could predict issues before doing the most expensive, time-consuming, and irreversible process steps that lead to high attrition or scrap?
In many cases, it is possible with predictive analytics. When data is captured and made actionable with an Arch System, the quality data you already have can be classified against the processing conditions used upstream. Because of the magnitude of data to be interpreted and the often subtle variations, human analysts may be unable to see the correlations. Machine learning on top of standardized Arch data empowers you to act.
Bring AI to your manufacturing line today.
“We’ve been talking about this a long time, but now we’re starting to act on it. Arch is helping predict defects upstream. Our work so far shows that attrition may be reduced by up to 33% as well as reducing the capacity needs on our downstream testing machines by only scanning parts predicted to fail inspection.”
– Engineering Manager, Electronics Manufacturer
Quality Prediction Models
Can a sewing machine predict the quality of the sewing job done on it? With Arch Systems, it can. Our POD-1C devices can read the motor encoder of an industrial sewing machine, derive stitches and seam data from the voltage pulses, and broadcast stitching patterns that can be classified against the quality of the job and analyzed for anomalies. As soon as an operator finishes sewing a piece, you can answer ‘Was it well done?’ with a red, yellow, or green quality rating.
Your legacy machines are no different. Whether a press where force provides a signature of the quality of the job or an oven where the actual temperature profile is the key to a good bake, what’s missing is the standardized collection of ground truth machine data correlated against outcomes. Arch’s analytics team can work with you to identify what quality vs anomalies means for your organization and how to design proper data collection and analysis to predict and improve quality.
“Look at the seam in your shirt sleeve. Does it pull out when you tug on it? How hard do you have to tug before it does? That can be predicted in real-time from machine and product data. If quality is a driving factor of your business, this capability is a game changer.”
– IT manager Textile Manufacturer
Predictive Analytics and Maintenance Models
Predictive maintenance uses data to predict in advance when something will break down. It prevents throwing away parts or machines that have useful life remaining. It also empowers you to prevent unplanned downtime by identifying and repairing or replacing machines or parts that have worn out sooner than expected.
Arch Systems is your partner to implement affordable, standardized data collection for machine cycles, downtime reasons, and performance anomalies, building up an easy-to-understand and actionable record of machine lifetime that can be translated into practical predictive maintenance. The end result can be over 10% saving on yearly CAPEX, which for many cost-conscious manufacturers is transformational.
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Manage your data with powerful and standardized Edge and Cloud servers, putting the power in your hand to maximize security and benefits. Unlock the value in your data by loading it into your organizations existing systems or 3rd party tools.
Analyze your data, both Arch-generated and existing, and elevate your current workflows from reactive to predictive by leveraging artificial intelligence and machine learning.
In many cases, the limiting factor for how quickly you can solve problems with data is how quickly you can put together a clean, unified dataset. Once you have clean data, it’s straightforward to get value from it.
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