A Windows desktop application that brings equipment-specific model APIs and local statistical analysis into a consistent user workflow.
Highlights
- Built a WPF/MVVM application supporting three equipment-specific modeling and analysis workflows.
- Implemented Python-based statistical analysis and integrated team-provided model APIs with progress and result visualization.
- Delivered the desktop application, installer, and operating documentation to a customer site.
Purpose
- Let equipment users prepare data, configure analysis, and inspect results without operating APIs or Python command-line tools directly.
- Support impeller, belt, and bearing analysis through one desktop application.
Implementation
- Organized views, parameters, shared settings, execution status, and results using WPF and MVVM.
- Coordinated data preparation, model requests, progress logs, and validation for the team-provided impeller model API.
- Implemented local Python workflows for belt and bearing analysis, including feature preparation, K-value evaluation, KMeans clustering, and statistical threshold generation.
- Exposed adjustable warning sensitivity and Any/All decision options through the application.
- Kept external API and local-process execution separate while presenting consistent progress, error, and completion states.
Delivery
- Completed the WPF application, Python statistical workflows, API integration, and installer.
- Delivered the application and operating documentation to one customer site.
- Integrated the team’s deep-learning model service; its training algorithm and server were provided by other team members.