Our manufacturing client, which specializes in industrial monitoring systems, was looking for ways to optimize its business-critical product defects analysis. They were struggling with data volume, various parameters and devices, and a significant amount of time-consuming manual inspection. VirtusLab helped create an ML-based solution to feed relevant data from the cloud into Power BI. The client gained a faster analysis solution that continuously improved accuracy.
Our client needed to thoroughly examine the test data of their product to detect faults in production. Data analysts monitored several parameters, types, and combinations of both to identify faults in the equipment being sold. The sheer volume of data made manual processing challenging, with 9200 device types and characteristics measured. Our client’s primary analysis tool displayed the measurement data over time and determined whether a product needed to be repaired or sent to packaging. However, the resulting charts of the analysis were far from easy to handle, and they could only analyze a fraction of the data.
The lack of information regarding which data points indicated faults in the system also extended the analysis time. In addition, the manufacturer’s current architecture included a SQL database on an Azure server and on-prem solutions for analytics purposes. It lacked the ability to scale, collect data from various sources quickly, and offer convenient access for the analytics team. This made it hard to change the approach to analysis. Consequently, our client approached VirtusLab for assistance in improving their semi-manual data inspection process.
Based on the client’s data analysis team’s experience and the database’s current location, VirtusLab suggested designing and implementing a cloud solution on Azure with Power BI extensions. The solution’s core revolves around a machine learning model responsible for identifying issues in the measurement data, making manual reviews unnecessary. To create the initial version of the neural network model, VirtusLab opted for a statistical drift detection model.
The system learns from feedback derived from data analytics and adjusts future alerts accordingly. VirtusLab developed a visualization platform using Power BI, which presents time series data for a specific device type, highlighting points where drift changes have been identified. A data analyst can then determine whether the drift signifies a problem, and the feedback is automatically sent to the Azure platform to enhance the model’s decision-making capabilities.
The human feedback drift detection process:
The platform is scalable and adaptable to future data sources and features. Its wide adoption is due to Microsoft’s provision of familiar infrastructure components to the client. Our client also gained:
Languages: Python, TensorFlow
Data visualization: Power BI
Infrastructure: Azure Data Factory, Azure Storage, Azure Machine Learning
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VirtusLab's work has met the mark several times over, and their latest project is no exception. The team is efficient, hard-working, and trustworthy. Customers can expect a proactive team that drives results.
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