Solution architecture: Defect prevention with predictive maintenance
Without a manufacturing-control system that is capable of identifying slowdowns or potential failures to improve the overall process, manufacturing companies can lose money and productivity on scrap and rework. Plus, wide-scale recalls can shake consumer confidence, further affecting your bottom line.
This solution introduces a quality-control process that helps predict failures in manufacturing pipelines (assembly lines), so your company can produce more while wasting less and saving money. It uses test systems that are already in place and failure data, specifically looking at returns and functional failures at the end of an assembly line. By combining these with domain knowledge and root-cause analysis within a modular design that encapsulates main processing steps, it provides an advanced-analytics solution that uses machine learning to predict failures before they happen.Catching future failures early allows for less expensive repairs or even discarding, which are usually more cost efficient than going through recall and warranty cost.
Deploy to Azure
Use the following pre-built template to deploy this architecture to AzureDeploy to Azure
|Stream Analytics provides near real-time analytics on the input stream from the Azure Event Hub. Input data is filtered and passed to a Machine Learning endpoint, finally sending the results to the Power BI dashboard.|
|Event Hubs ingests raw assembly-line data and passes it on to Stream Analytics.|
Machine Learning Studio
|Machine Learning predicts potential failures based on real-time assembly-line data from Stream Analytics.|
SQL Data Warehouse
|SQL Data Warehouse stores assembly-line data along with failure predictions.|
|Power BI visualises real-time assembly-line data from Stream Analytics and the predicted failures and alerts from Data Warehouse.|
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